{"as_of":"2026-08-10T12:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:274671d2bb8f49489f2e8d4e7cebbc9f6484c242a146fb40b915b99510b6a040","coverage":[{"denominator":41,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":41,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:09:56.505004Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2505.16078/citation-record","integrity":"/paper/2505.16078/integrity","json":"/paper/2505.16078/citation-record.json","paper":"/paper/2505.16078"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2004.05150","last_updated":"2020-12-02T17:52:35Z","snapshot_observed_at":"2026-07-31T17:17:17.205582Z","submitted_at":"2020-04-10T17:54:09Z","title":"Longformer: The Long-Document Transformer","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.05150","snapshot_observed_at":"2026-08-07T15:09:52.803608Z","title":"Peters, and Arman Cohan","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:52.803608Z"},"links":{"cited_paper":"/paper/2004.05150","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:b7b7bbb28efc5cf74b458eda1f862e3cebc3dc5fe1c3571cf090dfe92ce7ecdb","observation_id":"ebb90dad-de30-4a3b-9605-5b6bc0b81231","resolution":{"observed_at":"2026-08-07T15:09:52.803608Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-07T15:09:52.977920Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:52.977920Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:a3c111246fd8015702edeea51c1b8d9655ac2901dd4920c1af40d51a4bba76af","observation_id":"57be1821-85b8-409c-8c51-7326c5c173d4","resolution":{"observed_at":"2026-08-07T15:09:52.977920Z","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":"2026-08-07T15:09:53.053685Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.053685Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:5eb3935de398a2f2eedcb5a7a01352eaafbe2f8530450b771a312933ae29e54c","observation_id":"be81c185-fa08-4161-872c-8ccf462636da","resolution":{"observed_at":"2026-08-07T15:09:53.053685Z","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":"2026-08-07T15:09:53.134654Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.134654Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:0048ad8b2726ffbbbd32b7bfdb6b537251d1b4b0d80392488641304a762dc084","observation_id":"1113b452-759a-4b74-89b9-93cb02e9fb1b","resolution":{"observed_at":"2026-08-07T15:09:53.134654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.01910","last_updated":"2018-12-07T10:07:20Z","snapshot_observed_at":"2026-08-08T14:50:08.211668Z","submitted_at":"2018-11-05T18:39:54Z","title":"Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks","version":2},"cited_work":{"arxiv_id":"1811.01910","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.01910","snapshot_observed_at":"2026-08-07T15:09:58.532489Z","title":"Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks","venue":"cs.CL","work_id":"b930d77e-6653-48b1-8b01-17b6d6c8158b","year":2018},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.168566Z"},"links":{"cited_paper":"/paper/1811.01910","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:51474c756e5b94f8616cedd5dc64b92a4318131c0adbc13bf7efa5f2bc6880ba","observation_id":"da56d67b-407a-4b2f-8cfa-def08a2c4331","resolution":{"observed_at":"2026-08-07T15:09:58.572634Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:09:53.325075Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.325075Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:ac5f6834f672569017c3ec60da89a30111210f9b2f9a52b465478ebaf8a6bb4c","observation_id":"e8abeaa0-b3a0-4373-9aea-9dd2f8bbe51e","resolution":{"observed_at":"2026-08-07T15:09:53.325075Z","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":"10.18653/v1/2020.coling-main.544","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":null,"work_id":"22c37a5d-ec23-4ef5-91de-4395e8517606","year":2020},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.390168Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:6222c732c919c01e27473b4bf832711cc24b179599c53e422d215196c505b079","observation_id":"e3cb891a-5df6-4657-83c1-abc1f007fba7","resolution":{"observed_at":"2026-08-07T15:09:56.975482Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11807","last_updated":"2022-10-21T08:33:55Z","snapshot_observed_at":"2026-07-06T14:08:37.178824Z","submitted_at":"2022-10-21T08:33:55Z","title":"Is Encoder-Decoder Redundant for Neural Machine Translation?","version":1},"cited_work":{"arxiv_id":"2210.11807","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.11807","snapshot_observed_at":"2026-08-07T15:09:58.357988Z","title":"Is Encoder-Decoder Redundant for Neural Machine Translation?","venue":"cs.CL","work_id":"09088a45-345f-48ce-bb8c-b6738f674685","year":2022},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.444658Z"},"links":{"cited_paper":"/paper/2210.11807","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:7461110f9346418ea3e767b75e6c777cc6bc5ff12af959bccaa1ed857c6c2498","observation_id":"5518176e-9fd7-4a39-ace7-d16368d39387","resolution":{"observed_at":"2026-08-07T15:09:58.416149Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-07T15:09:53.535044Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.535044Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:7f81a7696228abefb136d71c7418cceb660559105eec391fc750b55a41cddce2","observation_id":"40a9931a-de85-4e4d-b9a1-3c757ba5a030","resolution":{"observed_at":"2026-08-07T15:09:53.535044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.09210","last_updated":"2023-02-18T02:11:36Z","snapshot_observed_at":"2026-08-09T14:17:01.779895Z","submitted_at":"2023-02-18T02:11:36Z","title":"How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.09210","snapshot_observed_at":"2026-08-07T15:09:53.633654Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.633654Z"},"links":{"cited_paper":"/paper/2302.09210","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:922e97e0b86b0a402158867e87feb8986bd06ff2db1fe96eab7ff0802672a55e","observation_id":"feb0859f-6396-46da-ad35-4f61479df4bf","resolution":{"observed_at":"2026-08-07T15:09:53.633654Z","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":"10.17877/de290r-5097","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"Technische Universität Dortmund Eldorado (Technische Universität Dortmund)","work_id":"8cea0286-2331-494d-82b6-a5d0938db928","year":1998},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.737744Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:c9bb7e8a25015d36d7845fc99e23939021337bb50d3912202fd20024b83f680b","observation_id":"c0cf11bd-318d-409a-a12f-d0a78fc218d0","resolution":{"observed_at":"2026-08-07T15:09:56.857972Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1408.5882","last_updated":"2014-09-03T03:09:02Z","snapshot_observed_at":"2026-08-08T00:12:31.795779Z","submitted_at":"2014-08-25T19:48:04Z","title":"Convolutional Neural Networks for Sentence Classification","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1408.5882","snapshot_observed_at":"2026-08-07T15:09:53.854168Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.854168Z"},"links":{"cited_paper":"/paper/1408.5882","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:e5c65a84666a595c6c856ff9abaa830d4b89ede2ff428366c2e6b3b48241d1f6","observation_id":"0ea797cd-b400-4ab2-90c8-e21dbd08bbba","resolution":{"observed_at":"2026-08-07T15:09:53.854168Z","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":"2026-08-07T15:09:53.953813Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:53.953813Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:9858650608cce76a6004ec6c1723eafec995f7dd400c01fcae7385aa2f2a9c78","observation_id":"76682cc1-f6b2-4c58-809d-b957495c25c0","resolution":{"observed_at":"2026-08-07T15:09:53.953813Z","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":"2026-08-07T15:09:58.993544Z","title":null,"venue":null,"work_id":"133f72e4-6413-489c-9530-e08bcd420d8f","year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.069001Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:169b312ea34240de9c9f65b1701fb69f9d441e211d0d77ab746948afd9c00731","observation_id":"577b2b46-c21c-443c-b24e-924655ec6217","resolution":{"observed_at":"2026-08-07T15:09:59.039668Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-06T15:24:34.790850Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-07T15:09:54.210845Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.210845Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:b0a3fa6294664f495f6cc31842d8ae2dc3f77157d31a98e08963c773de837a74","observation_id":"d26dd0ec-29fe-4856-92bf-a8dd8a188fa1","resolution":{"observed_at":"2026-08-07T15:09:54.210845Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13461","snapshot_observed_at":"2026-08-07T15:09:54.313169Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.313169Z"},"links":{"cited_paper":"/paper/1910.13461","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:1a579ed2c876c8bc60b8febc84cd2448da4a97304c667c71cb86c42b19728767","observation_id":"a547b08b-692f-4049-9b72-c6aa7202ba44","resolution":{"observed_at":"2026-08-07T15:09:54.313169Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.00190","last_updated":"2021-01-01T08:00:36Z","snapshot_observed_at":"2026-07-06T10:29:18.734092Z","submitted_at":"2021-01-01T08:00:36Z","title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.00190","snapshot_observed_at":"2026-08-07T15:09:54.446604Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.446604Z"},"links":{"cited_paper":"/paper/2101.00190","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:cd6094c9185839ebca3e9944453ae1dd38bf547fc713036d6f12df0ba6cbd103","observation_id":"a8eb9c18-15ea-4325-a52f-e09e404e3acf","resolution":{"observed_at":"2026-08-07T15:09:54.446604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T15:09:54.579104Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.579104Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:df851a35d260cdec30de7022cc162abcb85f67b9f8343b74919058dcbc325f75","observation_id":"23e81b8f-ee6b-4c3c-9ef2-cdeebb9b9a6b","resolution":{"observed_at":"2026-08-07T15:09:54.579104Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-07T15:09:54.672242Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.672242Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:d6994484f2feeb689bc28f359b43a9f2535ef6377eaf01b8597961d01e649a02","observation_id":"8c712384-404a-4ca8-ae67-201807b3f217","resolution":{"observed_at":"2026-08-07T15:09:54.672242Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.00405","last_updated":"2021-12-01T10:45:02Z","snapshot_observed_at":"2026-07-06T12:14:07.169525Z","submitted_at":"2021-12-01T10:45:02Z","title":"NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.00405","snapshot_observed_at":"2026-08-07T15:09:54.766681Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.766681Z"},"links":{"cited_paper":"/paper/2112.00405","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:4da2daf770e0ce907317cc1f1014655f62f91b6f70049146f9b897494ca42f9b","observation_id":"92c0df19-bd66-49aa-8310-ba2804f7b873","resolution":{"observed_at":"2026-08-07T15:09:54.766681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.06435","last_updated":"2024-10-17T01:10:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-07-12T20:01:52Z","title":"A Comprehensive Overview of Large Language Models","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.06435","snapshot_observed_at":"2026-08-07T15:09:54.864059Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.864059Z"},"links":{"cited_paper":"/paper/2307.06435","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:e934e0e9c5460f5e1d6c613edb23fb559d8cd0f01a6a4ed848a47ced8175cd76","observation_id":"79533b9f-55a5-4cfa-8fb7-1193df466ea2","resolution":{"observed_at":"2026-08-07T15:09:54.864059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.00558","last_updated":"2022-01-03T10:07:13Z","snapshot_observed_at":"2026-08-02T13:45:38.495569Z","submitted_at":"2022-01-03T10:07:13Z","title":"Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models","version":1},"cited_work":{"arxiv_id":"2201.00558","doi":null,"metadata_source":"pith","pith_arxiv_id":"2201.00558","snapshot_observed_at":"2026-08-07T15:09:57.636611Z","title":"Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models","venue":"cs.CL","work_id":"1b643149-0aa9-41c2-b598-d2e7d39ce8af","year":2022},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:54.957440Z"},"links":{"cited_paper":"/paper/2201.00558","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:a6d67b526a6d95e2a39b7e81c7afaf245e147136d10be258ffb35fe76bd56b79","observation_id":"aff8009c-3d58-4a0b-bef3-94c638e2cde0","resolution":{"observed_at":"2026-08-07T15:09:57.721189Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13296","last_updated":"2024-10-30T01:04:15Z","snapshot_observed_at":"2026-08-08T06:13:30.100547Z","submitted_at":"2024-08-23T14:48:02Z","title":"The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13296","snapshot_observed_at":"2026-08-07T15:09:55.095306Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.095306Z"},"links":{"cited_paper":"/paper/2408.13296","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:97d6b9caa2e829c5fbd9d0d1e685ec8be3376bc4c529478402b90aa66621f6a8","observation_id":"d34ece1a-8a34-4a56-9baa-0b71ec5b3013","resolution":{"observed_at":"2026-08-07T15:09:55.095306Z","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":"2026-08-07T15:09:58.854942Z","title":null,"venue":null,"work_id":"21061336-3600-4981-96ba-db4ee34168a2","year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.201111Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:15c338416dcbb68c066d66c140eca8e11a7b4c8daecf290373f4a85dfc630314","observation_id":"c1d4af74-e3d6-4c6e-a9f5-0d1191e2deb9","resolution":{"observed_at":"2026-08-07T15:09:58.915478Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10683","last_updated":"2023-09-19T15:14:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-10-23T17:37:36Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.10683","snapshot_observed_at":"2026-08-07T15:09:55.343678Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.343678Z"},"links":{"cited_paper":"/paper/1910.10683","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:7a7a1bcf9e9014206b9c1e5f8da8d61dd806b4622bd0da2afb5bea7af8a3a147","observation_id":"02c548c2-5900-4555-8375-2eee076f0328","resolution":{"observed_at":"2026-08-07T15:09:55.343678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07927","last_updated":"2025-03-16T06:23:34Z","snapshot_observed_at":"2026-08-05T17:55:26.008016Z","submitted_at":"2024-02-05T19:49:13Z","title":"A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07927","snapshot_observed_at":"2026-08-07T15:09:55.437139Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.437139Z"},"links":{"cited_paper":"/paper/2402.07927","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:76337614b1609d6578c5a1ca6d09d7c2e530a7d01b3d4e2c36b35d0658a8b326","observation_id":"cf542a7d-5772-4f05-8b1c-029aac770d0a","resolution":{"observed_at":"2026-08-07T15:09:55.437139Z","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":"2026-08-07T15:09:55.511039Z","title":"Yoo, Chan Yeun, Dirar Homouz, and Aya Taha","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.511039Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:52a0fddefb15d8be885b725595686711b8e9f770b143842258d2fd2da666d7a6","observation_id":"79fd70c6-ff55-4c16-a434-f964abc70e7c","resolution":{"observed_at":"2026-08-07T15:09:55.511039Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.00118","last_updated":"2024-10-02T15:22:49Z","snapshot_observed_at":"2026-08-02T16:20:09.773989Z","submitted_at":"2024-07-31T19:13:07Z","title":"Gemma 2: Improving Open Language Models at a Practical Size","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.00118","snapshot_observed_at":"2026-08-07T15:09:55.575378Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.575378Z"},"links":{"cited_paper":"/paper/2408.00118","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:8e0abd30ab12d3cfb746f4589b33b253d93ef6222a898330906c47eda8b913ad","observation_id":"2982c0c8-dff5-4f8c-b7d0-f5721580dcf4","resolution":{"observed_at":"2026-08-07T15:09:55.575378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-07T15:09:55.648113Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.648113Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:be1e5d0c7aad02b1da204b09372e685582c430a38cfeea765335f76813e2f9d4","observation_id":"cfe9a2c3-a940-47de-8976-c14cf210cbfe","resolution":{"observed_at":"2026-08-07T15:09:55.648113Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.11171","last_updated":"2023-03-07T17:57:37Z","snapshot_observed_at":"2026-07-06T12:50:22.773056Z","submitted_at":"2022-03-21T17:48:52Z","title":"Self-Consistency Improves Chain of Thought Reasoning in Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.11171","snapshot_observed_at":"2026-08-07T15:09:55.728373Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.728373Z"},"links":{"cited_paper":"/paper/2203.11171","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:5a54741304a0b55c8974b00ad9a4409c64b192330d770e3e5d3b8ef75645c329","observation_id":"de88bda2-a579-4dc4-a024-0789e6c3600a","resolution":{"observed_at":"2026-08-07T15:09:55.728373Z","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":"10.18653/v1/2023.findings-acl.489","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":null,"work_id":"e906698c-a067-459c-910f-ad289e28a001","year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.792425Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:59c16c3dd82d1f5729fb6005aa1b2e931354de4343cb6d875ec8dd0ad076b94f","observation_id":"4003bb40-3caf-4c9d-9ac0-1aacd69075b0","resolution":{"observed_at":"2026-08-07T15:09:56.726220Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.13663","last_updated":"2024-12-19T06:32:26Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-18T09:39:44Z","title":"Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.13663","snapshot_observed_at":"2026-08-07T15:09:55.862627Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.862627Z"},"links":{"cited_paper":"/paper/2412.13663","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:08e1930f5e269b8940a5aeffce4d76f84a3a365c8b432a41527fc2f875a38187","observation_id":"8a96b449-39b1-4486-b337-e99d300f7ec5","resolution":{"observed_at":"2026-08-07T15:09:55.862627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-07T15:09:55.950171Z","title":"Chi, Quoc Le, and Denny Zhou","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:55.950171Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:8c08f82b70a9e59c4d9b1cb1ecfc2a9af991d71311722001d8668d5f140a70f8","observation_id":"6123519c-c038-487d-9162-c5d6933d3b23","resolution":{"observed_at":"2026-08-07T15:09:55.950171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.18600","last_updated":"2025-03-03T17:08:21Z","snapshot_observed_at":"2026-08-07T17:49:06.764625Z","submitted_at":"2025-02-25T19:36:06Z","title":"Chain of Draft: Thinking Faster by Writing Less","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.18600","snapshot_observed_at":"2026-08-07T15:09:56.040064Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.040064Z"},"links":{"cited_paper":"/paper/2502.18600","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:64941f070e175429d2e1ff7cc293f6642c190f202decbd027bdf9233780a85f0","observation_id":"ba66ad8b-3e86-41e2-9819-2407bbab9182","resolution":{"observed_at":"2026-08-07T15:09:56.040064Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05661","last_updated":"2024-07-03T01:29:20Z","snapshot_observed_at":"2026-07-06T16:45:24.070954Z","submitted_at":"2023-11-09T08:00:32Z","title":"Prompt Engineering a Prompt Engineer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05661","snapshot_observed_at":"2026-08-07T15:09:56.120237Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.120237Z"},"links":{"cited_paper":"/paper/2311.05661","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:ec1797f4453659ab2723a6685a51ec7107ab4d97de0088847b9b2e6ecf0d1cfa","observation_id":"ca4836d0-f800-4b1a-b025-9a13bc96e4e0","resolution":{"observed_at":"2026-08-07T15:09:56.120237Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01898","last_updated":"2017-05-26T01:27:23Z","snapshot_observed_at":"2026-07-06T05:32:31.164578Z","submitted_at":"2017-03-06T14:40:09Z","title":"Generative and Discriminative Text Classification with Recurrent Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.01898","snapshot_observed_at":"2026-08-07T15:09:56.200252Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.200252Z"},"links":{"cited_paper":"/paper/1703.01898","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:dc29b8e27f5a63484236be1c59ceb861b71903e8e16991111681b7f01e63fb33","observation_id":"b2809f73-bd89-4bb1-9ac6-36b76fde5f26","resolution":{"observed_at":"2026-08-07T15:09:56.200252Z","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":"2026-08-07T15:09:58.743841Z","title":null,"venue":null,"work_id":"506aec87-b91a-4d2b-a10a-a1f4c0e27967","year":2023},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.265164Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:3793866a4988f6ae0a88b9b7d576540f847efac05f3486672d4089ab432b24f1","observation_id":"636c8604-34f3-48e7-89fb-f2fa259b6653","resolution":{"observed_at":"2026-08-07T15:09:58.772866Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18815","last_updated":"2024-11-10T12:49:15Z","snapshot_observed_at":"2026-08-08T01:26:31.350679Z","submitted_at":"2024-02-29T02:55:26Z","title":"How do Large Language Models Handle Multilingualism?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18815","snapshot_observed_at":"2026-08-07T15:09:56.309279Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.309279Z"},"links":{"cited_paper":"/paper/2402.18815","citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:31e275f927686fc3f47efddda62a560bbf8528dd95858ae6e122adcd6cb1b2e0","observation_id":"350949e1-89b0-4d9e-9b3f-fcab29ef3baf","resolution":{"observed_at":"2026-08-07T15:09:56.309279Z","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":"2026-08-07T15:09:56.383260Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.383260Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:275ab5d5ba2d51d5f0b4cce4c2ee5c35a3efa1c2203c8ffeb71ff6b316c4cf51","observation_id":"b11178c9-0731-4573-a590-e6b0e2d6201b","resolution":{"observed_at":"2026-08-07T15:09:56.383260Z","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":"2026-08-07T15:09:56.428483Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.428483Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:7f2da09cfda9c3f2b5498a4c9cb04c32b77cf4c264b7053b66e07df878b71c4b","observation_id":"61ce1bd4-dbb2-484e-b712-03e1cc341f7e","resolution":{"observed_at":"2026-08-07T15:09:56.428483Z","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":"2026-08-07T15:09:56.505004Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification","version":3},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T15:09:56.505004Z"},"links":{"citing_paper":"/paper/2505.16078"},"observation_digest":"sha256:2e4b9960ad19cda74a46c9a2a1a47183972262b6df17e3d7a1cf56eadbd0ec0b","observation_id":"c9246cad-1934-4f39-8348-fb9980eb4362","resolution":{"observed_at":"2026-08-07T15:09:56.505004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.16078","last_updated":"2025-06-23T20:09:36Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-08T01:26:58.491852Z","submitted_at":"2025-05-21T23:39:24Z","title":"Small Language Models in the Real World: Insights from Industrial Text Classification"},"reference_resolution":{"displayed":41,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":35,"verified_exact":6,"verified_fuzzy":0},"total_outbound_references":41},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.16078."}