{"as_of":"2026-08-14T13:33:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6876899246fb108b89b8f917127e9d1e724632838c3ebaf83199f24134b3241d","coverage":[{"denominator":90,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":90,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:12:55.407053Z","state":"measured"},{"denominator":90,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":90,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.00312/citation-record","integrity":"/paper/2506.00312/integrity","json":"/paper/2506.00312/citation-record.json","paper":"/paper/2506.00312"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:48.859735Z","title":"Influence of the movies on attitudes and be- havior,","venue":null,"work_id":null,"year":1947},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:48.859735Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:fd3431da692d96738fb16932cb8cf773e2ce436e235ef41f8b8e67ed7e5eff84","observation_id":"1fd68cfc-4b70-45c8-b735-ab094184ab73","resolution":{"observed_at":"2026-08-07T12:12:48.859735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:48.918202Z","title":"Tzioumakis and C","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:48.918202Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:84814dc0ee01989a48fdecdf603b7e387cad9c1d5f40f85e061a90c6a9c5b539","observation_id":"5a8b53ce-b10f-432c-a668-c99e030e6b76","resolution":{"observed_at":"2026-08-07T12:12:48.918202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:48.968699Z","title":"Movies, consumption markets & culture,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:48.968699Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:1cd12cfd1fe6f5ed0759a4c39ebb1d47d8fc76612869780c9fe4a5b04e16d179","observation_id":"ed1192ec-164c-47f4-b978-bcddd31fb8d4","resolution":{"observed_at":"2026-08-07T12:12:48.968699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.035789Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.035789Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f589ffb261b29b5c0d98e2c100728730ed1e3c2be55bd98cb21ec2d778a9bf8b","observation_id":"4bbd2a16-9e6d-4768-9cfb-0f9fed434004","resolution":{"observed_at":"2026-08-07T12:12:49.035789Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.098763Z","title":"The effect of movie portrayals on audience attitudes about nontra- ditional families and sexual orientation,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.098763Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:07a7bfe544be91561851780f00dcef95220f8a36377420b0e850ab0891b5d3ec","observation_id":"82eb9916-e122-4564-bbb1-c898e2a6c40f","resolution":{"observed_at":"2026-08-07T12:12:49.098763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.151020Z","title":"Natural language processing,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.151020Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:ac4fb5f6bb2d19ab624b9a26cbdda597e37f91a1d3475ad7c9df1510ac9e5ee8","observation_id":"26e28cbe-5387-4e11-8e9c-8d14c5f457d0","resolution":{"observed_at":"2026-08-07T12:12:49.151020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.223384Z","title":"Deep learning in clinical natural language processing: a methodical review,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.223384Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:ecfaba17d7c1bebcd1accc246952658be49ffc9d86a03a2605e605aae5b7df0d","observation_id":"1de23df2-a6ba-4ca6-99b1-c58e52da4178","resolution":{"observed_at":"2026-08-07T12:12:49.223384Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.281300Z","title":"Machine learning and nat- ural language processing: Review of models and opti- mization problems,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.281300Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:3366655c3afeca522b7009f85545094ed6678ffe7c42c882fe0f52d4d6adc8bc","observation_id":"38a29c3a-0b3c-402d-bb4b-f4d776d0435f","resolution":{"observed_at":"2026-08-07T12:12:49.281300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.370926Z","title":"Language models are few-shot learn- ers,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.370926Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:c6bf1e5ec0592898633d9811d2f66aba78a2e178e756e3404a0ea07fc3c289d0","observation_id":"fd954a6f-7ab1-4b61-94f5-ba6b8bb172df","resolution":{"observed_at":"2026-08-07T12:12:49.370926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.438797Z","title":"Sci- ence in the age of large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.438797Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:9730ca596fc85c68879a1d13730ec37cece5ab6b6dc3d1b8defd4a793009be96","observation_id":"1adefacb-d61b-46e7-aa6a-d78fdd14e500","resolution":{"observed_at":"2026-08-07T12:12:49.438797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:07.727421Z","title":"Gen- erative artificial intelligence: a systematic review and ap- plications,","venue":null,"work_id":"f62fd185-c434-4dd0-88ac-71fffe0add7e","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.530672Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:4c23a06d6b48a3c34be9da1f7a81aa095b3bb9103ecd03d7fe49344de3ba20a2","observation_id":"39a9578d-d15a-4e29-ac3a-2adda04f8160","resolution":{"observed_at":"2026-08-07T12:13:07.775670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:07.546520Z","title":"Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning,","venue":null,"work_id":"5223a4b3-4f56-4b03-997f-6e1f63205459","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.604231Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:b41fc10dfc0f34a6d7140e6cf8daacf1ac27691e38976a76518f779772b036bc","observation_id":"d736c738-48b7-4359-a6fb-5ee2254491ea","resolution":{"observed_at":"2026-08-07T12:13:07.664340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:07.437760Z","title":"The woman worked as a babysitter: Biases in language model generation,","venue":null,"work_id":"d87d521b-c2fc-4b96-a3cd-cc51ad6e838b","year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.669863Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:a4add088e1cc5d9617ac81a81d5f7e7caa52449461bc9d5c77b91b07a1da0526","observation_id":"73ffe2fc-4723-48aa-853c-f6dd0ae49a1c","resolution":{"observed_at":"2026-08-07T12:13:07.480225Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:49.726727Z","title":"A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.726727Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:3e727226e4128429df17068a39ddebb711f801c4d9bdadfb74adb6b9101a67dd","observation_id":"1d71e73b-ed6f-4420-b97f-8688a42ca7e2","resolution":{"observed_at":"2026-08-07T12:12:49.726727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.11193","last_updated":"2025-02-16T16:37:34Z","snapshot_observed_at":"2026-08-07T18:14:33.174400Z","submitted_at":"2025-02-16T16:37:34Z","title":"Large Language Models Penetration in Scholarly Writing and Peer Review","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.11193","snapshot_observed_at":"2026-08-07T12:12:49.778561Z","title":"Large language models penetration in scholarly writing and peer review,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.778561Z"},"links":{"cited_paper":"/paper/2502.11193","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:a82857ee4be983686b25b47e738827f0e645e0dbb8cbf2ac6105e26b3f177c04","observation_id":"11321aa7-c759-419b-a338-722074189818","resolution":{"observed_at":"2026-08-07T12:12:49.778561Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:07.330952Z","title":"A systematic review of large language models and their implications in medical education,","venue":null,"work_id":"6b07ee58-9801-45a4-8a37-e16f605dc732","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.848395Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6d21d44e55716772bbd9bc95faea79c10c5ff1c08912577e6b276cda31d7a2c8","observation_id":"e1526aeb-aed9-4a60-bb92-dee97006e92f","resolution":{"observed_at":"2026-08-07T12:13:07.378509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12819","last_updated":"2025-08-25T02:35:43Z","snapshot_observed_at":"2026-08-13T00:02:56.561615Z","submitted_at":"2024-05-21T14:24:01Z","title":"Large Language Models Meet NLP: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12819","snapshot_observed_at":"2026-08-07T12:12:49.917718Z","title":"Large language models meet nlp: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.917718Z"},"links":{"cited_paper":"/paper/2405.12819","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:806c55c9ef3447fff14013c163f6d6d9bfe32ed8f91eb4b74df8299058814ac1","observation_id":"91023965-ee3f-4a70-8f9e-7ffb3e946447","resolution":{"observed_at":"2026-08-07T12:12:49.917718Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:07.172168Z","title":"A review on large language models: Architectures, applications, tax- onomies, open issues and challenges,","venue":null,"work_id":"ea173207-bd0a-4642-be1c-9ab3c047d7b4","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:49.996828Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:cd572ff5e524fca050f37e7c4cfe26f04b92261a61ab6b751355242161470090","observation_id":"5c21de27-d964-4535-a26d-0f4bc5798694","resolution":{"observed_at":"2026-08-07T12:13:07.246320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:06.961047Z","title":"Language mod- els are unsupervised multitask learners,","venue":null,"work_id":"7ff56817-5de1-4845-b7df-27654a0827f9","year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.057159Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:fc06b4cc3ee23a0a0189b95bf0e1f57a2a86fcb9f07855d36c834a41fada6169","observation_id":"72411b3b-06b0-4c04-a8e0-83b0c5ac0426","resolution":{"observed_at":"2026-08-07T12:13:07.047102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:06.741052Z","title":"A survey on large language model (llm) security and privacy: The good, the bad, and the ugly,","venue":null,"work_id":"4e536065-22e3-4aaa-8cda-784409f07c55","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.116887Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:26141ba552af7dda98d1a224ab0b5f35c286ee4703804f191412cbfe373cdba6","observation_id":"3ede3a9a-2aa7-4a15-8059-e8630357784a","resolution":{"observed_at":"2026-08-07T12:13:06.828704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:06.527839Z","title":"Crafting clarity: Leveraging large language models to decode consumer reviews,","venue":null,"work_id":"7946d9c3-92a6-47e0-927b-425b65702800","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.176655Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:61111503b8a0b294b1afe3d117e234e284d1c3d22126df97c412f504255b0563","observation_id":"80e7bdd2-01c8-4cd9-aef7-801886b78ba8","resolution":{"observed_at":"2026-08-07T12:13:06.605652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:06.272720Z","title":"Consumer evaluations of movies on the basis of critics’ judgments,","venue":null,"work_id":"40d964ee-9e8b-4f66-b8c4-e590a86dd421","year":1999},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.238308Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:394a8b05d6879ad7aab6dbf0b9d47cac0367c3544280e9a59c872a5d66df06a6","observation_id":"1a9e1473-e220-4755-bfd8-e9e1c9c6d78e","resolution":{"observed_at":"2026-08-07T12:13:06.426494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:06.000815Z","title":"Dynamic ef- fects among movie ratings, movie revenues, and viewer satisfaction,","venue":null,"work_id":"cd73667b-eb55-4053-a6e5-332ef13ea32a","year":2010},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.291151Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:fd60804ea8d65a44107be2ef29c5ab24a104e2a8629306f7d93f2b87e383ee8b","observation_id":"1fe6fcd8-e258-496c-b5b2-2309a04b318a","resolution":{"observed_at":"2026-08-07T12:13:06.122841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:05.796392Z","title":"Kerrigan,Film Marketing","venue":null,"work_id":"91419e22-cf58-4616-84e0-17aaea510f0e","year":2017},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.365100Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:0ad5f5aea0f12e7d2e55a8cf4760f9ff519c7647c89bd594e0fb66402d7be0ec","observation_id":"6e189985-662f-47aa-8332-ea5f6d1d0cec","resolution":{"observed_at":"2026-08-07T12:13:05.885698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:05.514896Z","title":"The halo effect in multicomponent ratings and its implica- tions for recommender systems,","venue":null,"work_id":"bf0ad44f-51aa-4149-a863-3cb7bcd25e17","year":2012},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.471021Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f740439f55d2e9b9d58917ea57af8adb6d1e491f359b1826465b17f4c7d5149d","observation_id":"98ff0de5-79ca-4d4e-8738-013a1ef60d7a","resolution":{"observed_at":"2026-08-07T12:13:05.623826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:05.335457Z","title":"Understanding the influence of on- line movie reviews on audience reception: A data-driven approach,","venue":null,"work_id":"0661540f-d21f-4f55-aad5-e74774e01fb8","year":2018},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.517524Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:84c65c5ee07caace3cfe6e4ea0c7769b1bbcd690820106c62e2ce6f53abc9777","observation_id":"27dccb46-baa3-4b13-85b0-f4d15855242f","resolution":{"observed_at":"2026-08-07T12:13:05.415865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12351","last_updated":"2024-02-23T19:22:58Z","snapshot_observed_at":"2026-08-13T05:20:42.459383Z","submitted_at":"2023-11-21T04:59:17Z","title":"Advancing Transformer Architecture in Long-Context Large Language Models: A Comprehensive Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12351","snapshot_observed_at":"2026-08-07T12:12:50.553917Z","title":"Advancing transformer architecture in long-context large language models: A comprehensive survey,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.553917Z"},"links":{"cited_paper":"/paper/2311.12351","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:01a6e2f6de88976d520714275b6f1608e970f2c04e570afbb4c4d4c41a1f7809","observation_id":"549109f1-984c-40b6-ad79-94d29e4d7e45","resolution":{"observed_at":"2026-08-07T12:12:50.553917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07039","last_updated":"2023-10-10T21:56:58Z","snapshot_observed_at":"2026-08-13T05:52:38.664078Z","submitted_at":"2023-10-10T21:56:58Z","title":"Lipschitz Interpolation: Non-parametric Convergence under Bounded Stochastic Noise","version":1},"cited_work":{"arxiv_id":"2310.07039","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.07039","snapshot_observed_at":"2026-08-07T12:12:56.729505Z","title":"Lipschitz Interpolation: Non-parametric Convergence under Bounded Stochastic Noise","venue":"math.OC","work_id":"034635e4-9c8f-4e37-a405-7dd6f693668c","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.603528Z"},"links":{"cited_paper":"/paper/2310.07039","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:042250b327cfc203b86669dc6c7c025736928b91eea776bc073db5486bfdad65","observation_id":"cef2c369-6eef-4b96-87dc-9252465fd8f5","resolution":{"observed_at":"2026-08-07T12:12:56.817974Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:05.136072Z","title":"Large lan- guage models: a comprehensive survey of its applications, challenges, limitations, and future prospects,","venue":null,"work_id":"fb23aad6-9457-43e8-940c-4378f0ee27f3","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.637702Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:8981372007911666ddf207df3d0b0defe21c66e42027ed74545179bbe268ac83","observation_id":"d387ec07-b1a4-42fe-aa9f-f337dfd0ee5d","resolution":{"observed_at":"2026-08-07T12:13:05.219801Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.946951Z","title":"Auto- matic scoring of metaphor creativity with large language models,","venue":null,"work_id":"64453545-790f-4393-8ed8-ac122f040c4a","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.728138Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:2b541dd8cab4a0c89d449bb4963a870bbd98614e356644670436adb93ea49002","observation_id":"39d2899a-56a2-430b-8f84-50bf30af915e","resolution":{"observed_at":"2026-08-07T12:13:05.028351Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.766830Z","title":"Book review: Deep learning,","venue":null,"work_id":"35bb22cf-8d4f-411d-a302-77b696cdbd45","year":2016},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.773915Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:81c64e3135ffd8ffa4fa3ecc5c4b83686a13256a63dc25bb0a81cbad35630894","observation_id":"6fb70f4b-8057-4879-9600-de97a61e71a2","resolution":{"observed_at":"2026-08-07T12:13:04.848180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.592209Z","title":"Deep learning,","venue":null,"work_id":"ef133f69-c12d-41d1-80b7-eee263c9647c","year":2015},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.814467Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:30747db7808c46bb9e9f5daa02eb6cde16e7f50899dd04748d61f2dfed28b434","observation_id":"ac4f7c85-3c82-4773-bc35-9b748f640e0d","resolution":{"observed_at":"2026-08-07T12:13:04.673791Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:50.854796Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.854796Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:ae8d0ca460425caa15e90ea4ef3e3d3e4e6ac0f6c3d1cf0737e42485eb319e7a","observation_id":"1dd2f53c-f6a9-4699-9a64-6dca0bf99cca","resolution":{"observed_at":"2026-08-07T12:12:50.854796Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:50.923646Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.923646Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6a46439bc7341266d1bc279ba56ab9ba6aec82e024d3654c4196e4f3a00c8d3f","observation_id":"4dcd2097-e112-47b3-b5a8-d5cef8acfccb","resolution":{"observed_at":"2026-08-07T12:12:50.923646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.02017","last_updated":"2025-05-27T07:53:38Z","snapshot_observed_at":"2026-08-14T11:37:24.103698Z","submitted_at":"2023-03-27T21:27:58Z","title":"Unlocking the Potential of ChatGPT: A Comprehensive Exploration of its Applications, Advantages, Limitations, and Future Directions in Natural Language Processing","version":14},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.02017","snapshot_observed_at":"2026-08-07T12:12:50.979779Z","title":"Unlocking the potential of chatgpt: A compre- hensive exploration of its applications, advantages, limi- tations, and future directions in natural language process- ing,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:50.979779Z"},"links":{"cited_paper":"/paper/2304.02017","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:d361afa0d72d3d3856106975de696085a95a75121728881c7a3ae0fb525f0cfa","observation_id":"48d32294-56fa-4be0-a74e-7f8027b471a5","resolution":{"observed_at":"2026-08-07T12:12:50.979779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.450542Z","title":"Semantic language mod- els with deep neural networks,","venue":null,"work_id":"922d9eee-ffff-4c01-b93b-aaa8ac10991a","year":2016},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.052574Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:d78599f0ac6aad8000a4c9049a510b28146002f68c2d2506f171fd468d418be7","observation_id":"b9f8dc3d-281b-4f3d-bf81-73f48d272000","resolution":{"observed_at":"2026-08-07T12:13:04.508409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.298591Z","title":"Progress in neural nlp: modeling, learning, and reasoning,","venue":null,"work_id":"09f35951-09c2-409d-93f3-91c4e9f83d95","year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.135793Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f063602ecc49947eba71651df791615dbdfa2695eb744ca604ddb62ad844c130","observation_id":"b36ecc11-d67f-4722-be1c-235bfbff80f4","resolution":{"observed_at":"2026-08-07T12:13:04.363004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:04.119000Z","title":"De- pendency parsing with bottom-up hierarchical pointer net- works,","venue":null,"work_id":"2288fdf7-5f54-4338-bd50-094cebfd5ed7","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.221952Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:b4f92573de8954013f22d99faf257d9f088aa84c269b6ac668b298fd3ed456b2","observation_id":"dc8ec52d-7c28-4bb0-958e-1a7505d91c08","resolution":{"observed_at":"2026-08-07T12:13:04.209411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-07T12:12:51.280233Z","title":"Bert: Pre-training of deep bidirectional transformers for lan- guage understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.280233Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:494eeece3251db8d638de69d71b9e235e5568bef3de95633549d96309a6aac9b","observation_id":"5952c33c-7b8a-4b88-8b89-b3de6fd01e67","resolution":{"observed_at":"2026-08-07T12:12:51.280233Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.936415Z","title":"Improving language understanding by generative pre- training,","venue":null,"work_id":"2d36df84-ce1a-4ba1-9558-6ddde5510c99","year":2018},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.316517Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:9ef1f729f62c6de4507aaa19c7ada668aed87f092862ea1034463d2b050eda1a","observation_id":"e36b7d06-5080-4195-a34b-0eaac0141144","resolution":{"observed_at":"2026-08-07T12:13:04.023584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.750856Z","title":"Why does unsupervised pre-training help deep learning?","venue":null,"work_id":"0cb14fd6-4734-4e5a-bad7-b6f83e67baf1","year":2010},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.361557Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:d0a8f39e98382364572847ae896493e62202cd6d0da80c748d5daea4a814f993","observation_id":"40f302a1-7789-4c86-a731-706d0b7b443e","resolution":{"observed_at":"2026-08-07T12:13:03.829967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-07T12:12:51.465460Z","title":"Roberta: A robustly optimized bert pretraining approach,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.465460Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:4e5af4c665528f32a8a9908e8500390b46e6a1bb4c6c31c62c57fa79328f2e8f","observation_id":"12036114-173c-4aa6-b6b8-cfb620c41ae1","resolution":{"observed_at":"2026-08-07T12:12:51.465460Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.592070Z","title":"Hollywood movie subtitle dataset: Oscar nom- inations and top 10 blockbusters (1950–2024),","venue":null,"work_id":"3a589b7c-d20f-40fe-a9ee-3fc84fad7733","year":1950},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.563869Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:3313743f4b904e0fd10fa2bf499db422029311d7c20f8eb07ce3006766d33026","observation_id":"38d47076-fcbe-445e-b41d-0ae99f26349c","resolution":{"observed_at":"2026-08-07T12:13:03.666262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.424746Z","title":"Imdb movie reviews grouped by rat- ings,","venue":null,"work_id":"e192fd39-e23d-452a-9c5a-0b62dc059322","year":2025},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.671169Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:7cf6707af4f691855b2b5681fa9f66b568830e65f21eacc7389cf08208627309","observation_id":"731bcf65-d701-4f33-8a48-ce5a696743fa","resolution":{"observed_at":"2026-08-07T12:13:03.497676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.275501Z","title":"Subtitling and distribution of international films,","venue":null,"work_id":"2b3dc6cb-991b-4262-9489-3e484d50d33f","year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.806026Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:846ac1da4b57a79b15ede29ac83f22ad009ad5a8bc20a67f9a8a15af6149d1a5","observation_id":"083f146b-4559-4525-adce-c4b0171b7f3e","resolution":{"observed_at":"2026-08-07T12:13:03.334878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:03.007703Z","title":null,"venue":null,"work_id":"b166395e-7a8c-4ca9-92c8-dd4d27b9b152","year":2009},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.915879Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:a8558fa825fd30038eea14223cc0c429359da28db196d7e723a45d933f6e01d2","observation_id":"90428a95-a7cf-4049-a338-fcfc97ff3119","resolution":{"observed_at":"2026-08-07T12:13:03.141741Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:02.812040Z","title":"A large-scale comparison of historical text normalization systems,","venue":null,"work_id":"d4463512-b064-4828-a807-27c6b8af82b4","year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:51.979061Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:a8770162c0e658513ad0d8943594e532638d073075f95d6b165907ae050ba895","observation_id":"3e645950-a7c5-4360-ac73-5dd9d2ad5e0e","resolution":{"observed_at":"2026-08-07T12:13:02.932030Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:02.493415Z","title":null,"venue":null,"work_id":"5d671e83-73a7-4b86-a5ee-0fb36ece125f","year":2008},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.078361Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:c0dcc397c64e6c6db38652fa8dacc11ae181f505cd4248f9c7e3f4c5284bd3e4","observation_id":"19bf0c46-2c9b-4feb-ad11-ad9b699e19e3","resolution":{"observed_at":"2026-08-07T12:13:02.697943Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:02.293422Z","title":"Gpt-4o technical report,","venue":null,"work_id":"2ececd36-dc90-4f60-a062-ee2db21537d7","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.132141Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:dfdf65911a60dbfeabebd3ce4edfb562bde08295e567add0f40fb5d6129bc33b","observation_id":"b402224d-b8f9-48c2-b055-ae5cb7df83db","resolution":{"observed_at":"2026-08-07T12:13:02.400445Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:02.165254Z","title":"Chatgpt: Optimizing language models for dia- logue,","venue":null,"work_id":"950f67c0-79a8-4b99-be06-be92bbe6d9c1","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.186828Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:5dce99ea47c01b91001315c217b8be10feb2d5391a312686602c9501b4c4f71d","observation_id":"a7c38cc8-bef5-4827-8978-522d874ba9ac","resolution":{"observed_at":"2026-08-07T12:13:02.247471Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.03062","last_updated":"2023-05-04T08:04:24Z","snapshot_observed_at":"2026-08-13T11:49:32.942945Z","submitted_at":"2023-05-04T08:04:24Z","title":"A Serious Game for Simulating Cyberattacks to Teach Cybersecurity","version":1},"cited_work":{"arxiv_id":"2305.03062","doi":null,"metadata_source":"pith","pith_arxiv_id":"2305.03062","snapshot_observed_at":"2026-08-07T12:12:56.494936Z","title":"A Serious Game for Simulating Cyberattacks to Teach Cybersecurity","venue":"cs.CR","work_id":"3ed6fc58-254e-4f4a-b827-6a24a7c48629","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.256389Z"},"links":{"cited_paper":"/paper/2305.03062","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:14bb621c3afb92cec377e3a91ed4576e2d74c610eb0158a197ef65ae0b95c66f","observation_id":"c5d6bfa0-2b0b-4d94-aa49-9b1692f13d7b","resolution":{"observed_at":"2026-08-07T12:12:56.581381Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.05620","last_updated":"2023-12-09T17:28:01Z","snapshot_observed_at":"2026-08-13T15:26:47.913936Z","submitted_at":"2023-12-09T17:28:01Z","title":"Geometric constructions of small regular graphs with girth 7","version":1},"cited_work":{"arxiv_id":"2312.05620","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.05620","snapshot_observed_at":"2026-08-07T12:12:56.319636Z","title":"Geometric constructions of small regular graphs with girth 7","venue":"math.CO","work_id":"fb2765f7-7316-4b9e-a67c-6f52c04cc213","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.337910Z"},"links":{"cited_paper":"/paper/2312.05620","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:362789c3bbd44dd5662b1132367bd1b5accac1e806b14b39b1d0b4ca15b63cb4","observation_id":"333720a7-61a7-40bd-9af8-712fe9acb90b","resolution":{"observed_at":"2026-08-07T12:12:56.397462Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12345","last_updated":"2025-06-17T20:37:32Z","snapshot_observed_at":"2026-08-13T04:37:25.423033Z","submitted_at":"2024-01-22T20:20:48Z","title":"Distributionally Robust Receive Combining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12345","snapshot_observed_at":"2026-08-07T12:12:52.430670Z","title":"Scaling mixture-of-experts for multilingual reasoning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.430670Z"},"links":{"cited_paper":"/paper/2401.12345","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:562b66fdfbc8667c860d18474a8d8f11ce0b527ddf53997003c55ec8fd2fe008","observation_id":"3df6fae6-8eca-44f7-9379-b4d496009627","resolution":{"observed_at":"2026-08-07T12:12:52.430670Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14710","last_updated":"2024-04-03T09:15:15Z","snapshot_observed_at":"2026-08-13T11:34:55.306116Z","submitted_at":"2023-05-24T04:27:21Z","title":"Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14710","snapshot_observed_at":"2026-08-07T12:12:52.493476Z","title":"Multi-token prediction improves language modeling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.493476Z"},"links":{"cited_paper":"/paper/2305.14710","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:94419272d6edb0abe0c1eec5926edcd87eba947c7d3e5e2b30d8d995e48a8021","observation_id":"a6fe6a95-c848-47f4-98ec-9dc79e418761","resolution":{"observed_at":"2026-08-07T12:12:52.493476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2402.67890","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:56.106890Z","title":"A comparative evaluation of gemini and gpt-4 on multimodal educational tasks,","venue":null,"work_id":"703d077e-1652-4d96-b9f8-e0f4fdf87d28","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.535008Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:59898af9c18a5a1faf676f6984c6b06ce785360c4b9ab2587437a2cca4395308","observation_id":"3d959b9b-6a80-483d-9465-6752fd8fc26c","resolution":{"observed_at":"2026-08-07T12:12:56.178399Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02837","last_updated":"2024-02-05T09:48:07Z","snapshot_observed_at":"2026-08-13T04:26:57.473331Z","submitted_at":"2024-02-05T09:48:07Z","title":"With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations","version":1},"cited_work":{"arxiv_id":"2402.02837","doi":null,"metadata_source":"pith","pith_arxiv_id":"2402.02837","snapshot_observed_at":"2026-08-07T12:12:55.814639Z","title":"With a Little Help from my (Linguistic) Friends: Topic Segmentation of Multi-party Casual Conversations","venue":"cs.CL","work_id":"754d4479-de6d-47b8-9191-00c81a449916","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.623753Z"},"links":{"cited_paper":"/paper/2402.02837","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:0301fad5fc85dc53aa2346fddc27d3ad185dba42121f0e6b76b7b2c630fdc4f1","observation_id":"dc6692b2-b5a1-4ec8-9bc5-5321ce52a20e","resolution":{"observed_at":"2026-08-07T12:12:55.871769Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:02.073406Z","title":"N-gram-based text categorization,","venue":null,"work_id":"a4ebd484-27be-48ad-8ea4-2ef47c12029f","year":1994},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.693987Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:c963d09f2dd7cac51fa84f05b560328feb4b0e7ffd1d80e932c9ec55f64c0fc0","observation_id":"5587257d-7e2d-4610-964d-c1ab5085d3fe","resolution":{"observed_at":"2026-08-07T12:13:02.112537Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:01.976434Z","title":"Jurafsky and J","venue":null,"work_id":"f761d17a-cc53-49e3-a6a1-e6ec3a27c6d3","year":2025},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.796557Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:2c98e7b98edc0ae58614f39d22072f95fb65517ee0f45d323e959c6d49724a4f","observation_id":"5e4cb745-55a7-4a55-8446-23792de21334","resolution":{"observed_at":"2026-08-07T12:13:02.028375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:01.759661Z","title":"Document-level sentiment classification using hybrid machine learning ap- proach,","venue":null,"work_id":"f4c90025-3ef5-419a-aac1-7e6c5ee36c64","year":2017},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.910184Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:e6fadcf605ff5a552e42236c10e7befee1bc26e62ca790a2ff1f813f46d06afb","observation_id":"8443d60a-c6ac-4e95-ab70-7b1f8a548290","resolution":{"observed_at":"2026-08-07T12:13:01.862848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:01.464925Z","title":"Syntactic n-grams as machine learning features for natural language processing,","venue":null,"work_id":"0e4a0f71-6958-4c6d-a178-67b3f5d19f0f","year":2014},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:52.979872Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:00e65090deb0bd08cb24c1b996390df3bbdd8a67e7d1b5c37acf39d6eb48d3b0","observation_id":"369cdeba-b9c0-4c77-8558-4244b654ba54","resolution":{"observed_at":"2026-08-07T12:13:01.600337Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:01.247359Z","title":"Sentiment classification with roberta and data augmentation techniques,","venue":null,"work_id":"e07b9bec-edab-4b19-9175-9ccc29ddcd8c","year":2021},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.047458Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:083fd8f086720a6799f185db65e41c9de9330fc5a29e2564f1b86bde11213493","observation_id":"30210cc4-7e61-488d-87e3-907e4e4a1ca0","resolution":{"observed_at":"2026-08-07T12:13:01.361727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2009.07297","last_updated":"2020-09-15T18:00:18Z","snapshot_observed_at":"2026-08-07T18:17:06.049600Z","submitted_at":"2020-09-15T18:00:18Z","title":"Continuous measurements for control of superconducting quantum circuits","version":1},"cited_work":{"arxiv_id":"2009.07297","doi":null,"metadata_source":"pith","pith_arxiv_id":"2009.07297","snapshot_observed_at":"2026-08-07T12:12:55.643755Z","title":"Continuous measurements for control of superconducting quantum circuits","venue":"quant-ph","work_id":"b4f8697f-7531-4a7e-ba59-315e00a0b043","year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.075668Z"},"links":{"cited_paper":"/paper/2009.07297","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:fb860beb5778545af0a6cd1c61ffc405038010abbd95f2aba910f4b8808bbcda","observation_id":"69b8e2f4-5808-4426-8579-46ae2f772563","resolution":{"observed_at":"2026-08-07T12:12:55.702899Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:00.960442Z","title":"A statistical interpretation of term specificity and its application in retrieval,","venue":null,"work_id":"91246096-56ac-4945-ba13-e93a8c8bff10","year":1972},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.157252Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6dca00b632870c7b39f7a40739ddd3d48591d82caa1fe8afdcfc41f40d2c5fa6","observation_id":"c41afe79-4626-49d3-83ee-05b49bd62b19","resolution":{"observed_at":"2026-08-07T12:13:01.144019Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:00.712420Z","title":"An information-theoretic perspective of tf– idf measures,","venue":null,"work_id":"c244bbf9-30b9-4ef6-93ff-de4bad3779bf","year":2003},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.244302Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:fe5d300157092927128a131c1359ac71d694ad6364e8364986fb725022f795d0","observation_id":"eead8834-5128-446e-beb4-f7b1606037f2","resolution":{"observed_at":"2026-08-07T12:13:00.834054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:00.397007Z","title":"Tweeteval: Unified benchmark and comparative evaluation for tweet classification,","venue":null,"work_id":"b72908c0-39e7-4ad7-8912-28258f5fbaef","year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.324605Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:82a353897e38a398eb385fb5309fe6f48f0b247baf8a730f99e759b84108857c","observation_id":"886622ce-bf9b-4377-9f44-ca77f0158b8b","resolution":{"observed_at":"2026-08-07T12:13:00.596055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:13:00.171492Z","title":"Goemotions: A dataset of fine-grained emotions,","venue":null,"work_id":"28177864-4429-4331-889b-9e692840fb5e","year":2020},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.403711Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f6bb2a205acd68d327e3eb28c6c28e13f595ebf0e014a6adb33ca42910044fde","observation_id":"4651b243-a1e1-40f6-b6da-3cbca51189e4","resolution":{"observed_at":"2026-08-07T12:13:00.244067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:59.902152Z","title":"Defending against neu- ral fake news,","venue":null,"work_id":"0c96be3c-c8e6-4cdc-b160-d9fe4f8aaee1","year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.456939Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:8a7a75c2eecd9bf6ca452889c93782bcd3d1feb096cc97ad05bfb277eb8b8f81","observation_id":"286405ac-bcc1-4a04-9b20-0ba8c7a09279","resolution":{"observed_at":"2026-08-07T12:13:00.016339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:59.447379Z","title":"What makes good in-context examples for gpt-3?","venue":null,"work_id":"08578de3-6dbb-433f-9bf1-d98e19939156","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.507674Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:4f740020c800eff7e939658336ca6083ed3d50938c256c8f1e9d9eb193cd2295","observation_id":"faa62dc7-4391-4008-b4d6-de626c3f3d22","resolution":{"observed_at":"2026-08-07T12:12:59.743668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:59.158354Z","title":"Persona prompt- ing for controllable and diverse text generation,","venue":null,"work_id":"2a4e9e95-f0ec-432f-b4d4-947c5223b14b","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.593779Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:97f0ecb0f83bf243c17b11c616f10c53215b1f3e0f0c4c57011e9f63a623b6a2","observation_id":"4ee33c10-0db1-447d-93c3-19f004303b68","resolution":{"observed_at":"2026-08-07T12:12:59.288143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.932524Z","title":"Chunk-based sentiment analy- sis for long documents using transformer models,","venue":null,"work_id":"1e6f4442-5f5e-464e-b0e3-f43e084ac6e9","year":2022},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.709527Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6d0a2a38b8a42532248d819b2bfed9a3cddb72a00755fffcd6bec6476ae28972","observation_id":"a85182cb-54d4-42c0-a43b-cce648242e80","resolution":{"observed_at":"2026-08-07T12:12:59.031304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.713318Z","title":"Learning word vectors for sentiment analysis,","venue":null,"work_id":"5e98dc34-dfa5-4b1a-9c9c-910412a87d94","year":2011},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.833230Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:9928b20bbebb29eac73ce169e179b257fa1b3b73563215187219dde17d4abee2","observation_id":"e92d526f-87e7-48ee-bea3-02e47bfeea04","resolution":{"observed_at":"2026-08-07T12:12:58.783476Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.470889Z","title":"Explainable prompt learning for movie review sentiment analysis,","venue":null,"work_id":"d46f2125-8cfd-4afe-b542-4fa08a35c6e9","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:53.929077Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:acc28a65828c7564421fc248c0b335c74c0253745cf43a8a85657bd7712cf734","observation_id":"1c8c1f6f-f0ef-47ec-9bd4-e5d22338efd5","resolution":{"observed_at":"2026-08-07T12:12:58.567697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.00001","last_updated":"2023-02-27T22:09:35Z","snapshot_observed_at":"2026-08-13T12:35:47.840646Z","submitted_at":"2023-02-27T22:09:35Z","title":"Reward Design with Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00001","snapshot_observed_at":"2026-08-07T12:12:54.004545Z","title":"Emotionally informed language mod- els: A study on emotion bias in text generation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.004545Z"},"links":{"cited_paper":"/paper/2303.00001","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:15d861d74b6bd0b8c2983cd50fcd201e7ea00f7d19481e9db501972647c7e7f7","observation_id":"89208768-7722-478d-a081-5d8dfc6c7f02","resolution":{"observed_at":"2026-08-07T12:12:54.004545Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.06836","last_updated":"2024-08-07T08:09:53Z","snapshot_observed_at":"2026-08-13T04:43:49.988138Z","submitted_at":"2024-01-12T16:42:10Z","title":"Enhancing Emotional Generation Capability of Large Language Models via Emotional Chain-of-Thought","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.06836","snapshot_observed_at":"2026-08-07T12:12:54.099733Z","title":"Enhancing emotional generation capability of large language models with emotional chain- of-thought,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.099733Z"},"links":{"cited_paper":"/paper/2401.06836","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:9cf3dedc2aee71804004408952519594dec9347d6df81c3fd035815470aea2b7","observation_id":"c2ff2a03-e66f-4815-b63c-435eda8d4011","resolution":{"observed_at":"2026-08-07T12:12:54.099733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.318428Z","title":"More than a feeling: Accuracy and application of emo- tion analysis,","venue":null,"work_id":"01a1dd72-7257-47f1-970e-f8ebfe6fc80b","year":2022},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.172238Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:d59bf1753a0b43278ec23c40b23f007e2c956a139190de9bd5f2e4ad1f5ce705","observation_id":"d038704f-8fab-4efc-b1b7-739f7768149c","resolution":{"observed_at":"2026-08-07T12:12:58.355740Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.01876","last_updated":"2023-11-03T12:35:29Z","snapshot_observed_at":"2026-08-14T10:40:51.632787Z","submitted_at":"2023-11-03T12:35:29Z","title":"Sentiment Analysis through LLM Negotiations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.01876","snapshot_observed_at":"2026-08-07T12:12:54.260380Z","title":"Sentiment analysis through llm negotia- tions,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.260380Z"},"links":{"cited_paper":"/paper/2311.01876","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6c0a2cf7c0a8b7f42e20f97fe529f71515537e7610b2a83a148287f141a3b65d","observation_id":"bea2d283-0f40-4bd2-830c-cb92ee2dd42d","resolution":{"observed_at":"2026-08-07T12:12:54.260380Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.182145Z","title":"Audio-visual sentiment analysis for learning emotional arcs in movies,","venue":null,"work_id":"e84258d9-ff85-48ff-b82b-8a561781503f","year":2017},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.363017Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:dd55d1b271db24be3cf3c57ab0b432d4c5642324e830ed1b6c57cf852ab593d3","observation_id":"d6a9cd7e-41d0-4d7f-86aa-247d4f659f6d","resolution":{"observed_at":"2026-08-07T12:12:58.258274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:58.054672Z","title":"Multimodal deep models for predicting affective responses evoked by movies,","venue":null,"work_id":"8cd3dc3c-da91-40e9-86a9-969c5c72ab8b","year":2019},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.437701Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:97aaa769595e57e856f1e62e0c5e18b25b8c4051ff14861e7d13a57457150875","observation_id":"2c06d9e2-b201-45fd-9bdd-710deeb88bec","resolution":{"observed_at":"2026-08-07T12:12:58.113885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.866440Z","title":"Can ai write a movie review? a comparative study of human and machine criticism,","venue":null,"work_id":"8c6602a1-7e6d-4e54-ace4-8129dbbe0c57","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.505125Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:b146bae71e697666446acb6e652d10e9ad6a444a283f8f5c708d1c15ce0844da","observation_id":"a403f881-44ed-400a-b392-02c943981fce","resolution":{"observed_at":"2026-08-07T12:12:57.997948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.712482Z","title":"All that’s ’hu- man’ is not gold: Evaluating human evaluation of gener- ated text,","venue":null,"work_id":"5f99b66e-11b1-4ad2-b819-19391d07925a","year":2021},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.586904Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:04c10b30a9f48477a0beb2703c76c6b5893cc4081a10a36bdfbf8dd61e898644","observation_id":"7bc5366a-6ead-413b-bdf9-ab40681500e7","resolution":{"observed_at":"2026-08-07T12:12:57.767577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.02076","last_updated":"2025-01-23T00:52:08Z","snapshot_observed_at":"2026-08-12T22:52:07.949567Z","submitted_at":"2024-09-03T17:25:54Z","title":"LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.02076","snapshot_observed_at":"2026-08-07T12:12:54.660259Z","title":"Longgen- bench: Benchmarking long-form generation in long con- text llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.660259Z"},"links":{"cited_paper":"/paper/2409.02076","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:736a5e9bce736f271a0224a850dc8ed2d160be1e62238cbec7587eba01c0f40b","observation_id":"cbde4b25-e9b2-449b-b3d4-c7256cbe1d4d","resolution":{"observed_at":"2026-08-07T12:12:54.660259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15005","last_updated":"2023-05-24T10:45:25Z","snapshot_observed_at":"2026-08-13T11:34:36.774792Z","submitted_at":"2023-05-24T10:45:25Z","title":"Sentiment Analysis in the Era of Large Language Models: A Reality Check","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15005","snapshot_observed_at":"2026-08-07T12:12:54.731819Z","title":"Sen- timent analysis in the era of large language models: A reality check,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.731819Z"},"links":{"cited_paper":"/paper/2305.15005","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f87fdb8f216caaaae06c00a8e3d7b53ed836c44c2403f950e3975e0ccd29d253","observation_id":"adf39d5e-bce4-48db-98d4-7638a0434870","resolution":{"observed_at":"2026-08-07T12:12:54.731819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.554093Z","title":"Multimodal sentiment analysis: a survey of methods, trends, and challenges,","venue":null,"work_id":"070a0e9a-4c7c-4e70-8988-454cc4cad8c3","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.832798Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:d96345c4c16931d863d419cddd0250b0a39ff6462d6b3c9a985db996e6780dbf","observation_id":"dcb68610-5dfa-43d4-ac3b-046d073a9047","resolution":{"observed_at":"2026-08-07T12:12:57.628709Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.446439Z","title":"Multimodal few-shot learning with frozen language models,","venue":null,"work_id":"d2a95dc3-7f74-4fb3-b75d-08f5655840f3","year":2021},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.889247Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:8cec8f3ad5cf329ea2df4e78614a72fd4506d0e20f4c502f2d9c4e6266cdcd96","observation_id":"d71e381a-9dac-4825-986d-a35f2050cf86","resolution":{"observed_at":"2026-08-07T12:12:57.503995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.08068","last_updated":"2024-08-16T10:50:45Z","snapshot_observed_at":"2026-08-12T23:44:44.813057Z","submitted_at":"2024-06-12T10:36:27Z","title":"Large Language Models Meet Text-Centric Multimodal Sentiment Analysis: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.08068","snapshot_observed_at":"2026-08-07T12:12:54.958270Z","title":"Large language models meet text-centric multimodal sentiment analysis: A sur- vey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:54.958270Z"},"links":{"cited_paper":"/paper/2406.08068","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:9b8ac962c40baef8f6ba5d130e67a97e7c34e18c849e03a5aa0110079e889b88","observation_id":"c97fd0db-1f72-40e1-a6da-e55495935bad","resolution":{"observed_at":"2026-08-07T12:12:54.958270Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.337916Z","title":"A review on methods and applications in mul- timodal deep learning,","venue":null,"work_id":"1a203f27-48a8-43ed-b1d9-f061b24b4280","year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:55.058626Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:6f2eaeb8747b260c58ad79c18bc21f6335f5c4eb098f3445c7c679193013222f","observation_id":"9eb8d8e0-18b9-4410-b5b7-06fec20281bc","resolution":{"observed_at":"2026-08-07T12:12:57.355354Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.154451Z","title":"Blend: A benchmark for llms on everyday knowl- edge in diverse cultures and languages,","venue":null,"work_id":"c104f39d-bbbd-42bc-ac1e-3f2f3aa40287","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:55.151374Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:c7ffb98d55fd560d639096b20af3c50ccd55e40ae0438bf05874d805dd9562dc","observation_id":"1a7cca11-369e-403a-96e6-7955a0fba091","resolution":{"observed_at":"2026-08-07T12:12:57.239657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00860","last_updated":"2024-10-30T16:37:50Z","snapshot_observed_at":"2026-08-12T22:11:37.454852Z","submitted_at":"2024-10-30T16:37:50Z","title":"Survey of Cultural Awareness in Language Models: Text and Beyond","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00860","snapshot_observed_at":"2026-08-07T12:12:55.218323Z","title":"Sur- vey of cultural awareness in language models: Text and beyond,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:55.218323Z"},"links":{"cited_paper":"/paper/2411.00860","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:f1bb7ae5a42d0bc77ab8a4099ad1443acf9137bd9b4c27e8fd4e577ec7f8304f","observation_id":"87a6a1c8-b027-40f0-8502-3a1d505766b4","resolution":{"observed_at":"2026-08-07T12:12:55.218323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T12:12:57.035721Z","title":"Bertaqa: How much do language models know about local culture?","venue":null,"work_id":"c807add1-59b6-433f-acdb-4f53eb5c5c9d","year":2024},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:55.298570Z"},"links":{"citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:df0bff32b8cd423fecfd56371f58d2d193252b78fa7711b1dfe7889530c9be3e","observation_id":"ac789fef-2346-4542-acb7-7a78d6031e01","resolution":{"observed_at":"2026-08-07T12:12:57.077807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07462","last_updated":"2024-02-13T09:10:29Z","snapshot_observed_at":"2026-08-13T10:13:57.785384Z","submitted_at":"2023-09-14T06:41:58Z","title":"Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07462","snapshot_observed_at":"2026-08-07T12:12:55.407053Z","title":"Are large language model-based evaluators the solution to scaling up multilingual evaluation?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T12:12:55.407053Z"},"links":{"cited_paper":"/paper/2309.07462","citing_paper":"/paper/2506.00312"},"observation_digest":"sha256:5739cb90d2995c370e02da3018cb24a801cb03d932d1b9d8a92e65e909cbba64","observation_id":"5d8d1fe6-b626-431f-95cd-d7a75741ab34","resolution":{"observed_at":"2026-08-07T12:12:55.407053Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.00312","last_updated":"2025-05-30T23:45:53Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T13:45:10.678672Z","submitted_at":"2025-05-30T23:45:53Z","title":"An evaluation of LLMs for generating movie reviews: GPT-4o, Gemini-2.0 and DeepSeek-V3"},"reference_resolution":{"displayed":90,"state_counts":{"malformed_identifier":0,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":31,"verified_exact":2,"verified_fuzzy":53},"total_outbound_references":90},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 90 of 90 outbound references and 0 inbound Pith citation observations for arXiv:2506.00312."}