{"as_of":"2026-08-13T09:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:192070c9cb8fd258351c1e6d0976fdb16f488b169dfe06130ce43f6933e9fb60","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T16:17:07.599252Z","state":"measured"},{"denominator":49,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":49,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T15:25:46.925378Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T10:36:02.611276Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"cited_work":{"arxiv_id":"2412.10220","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.10220","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How good is my story? towards quantitative metrics for evaluating LLM-generated XAI narratives","venue":null,"work_id":"256b1553-28cf-4aad-be81-5ab0b9a7817a","year":2024},"citing_paper":{"arxiv_id":"2604.12543","last_updated":"2026-04-14T10:15:57Z","snapshot_observed_at":"2026-08-13T08:41:23.117710Z","submitted_at":"2026-04-14T10:15:57Z","title":"A Two-Stage LLM Framework for Accessible and Verified XAI Explanations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T15:25:46.925378Z"},"links":{"cited_paper":"/paper/2412.10220","citing_paper":"/paper/2604.12543"},"observation_digest":"sha256:ee888c30d464e35bcefb638e84a736bfbd3b3a1ecd36e942927a28fe16511348","observation_id":"3ecd87ac-c046-4939-811b-e477401c76f1","resolution":{"observed_at":"2026-05-11T10:36:02.614307Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"cited_work":{"arxiv_id":"2412.10220","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.10220","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"How good is my story? towards quantitative metrics for evaluating LLM-generated XAI narratives","venue":null,"work_id":"256b1553-28cf-4aad-be81-5ab0b9a7817a","year":2024},"citing_paper":{"arxiv_id":"2604.18311","last_updated":"2026-04-20T14:17:39Z","snapshot_observed_at":"2026-07-06T23:05:13.178333Z","submitted_at":"2026-04-20T14:17:39Z","title":"On the Importance and Evaluation of Narrativity in Natural Language AI Explanations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T05:21:12.989059Z"},"links":{"cited_paper":"/paper/2412.10220","citing_paper":"/paper/2604.18311"},"observation_digest":"sha256:962e0a3c3ab09d6add4e158e0884c8ff81a2cd70009e7382a81acc2a6c96efcb","observation_id":"94a37fcc-18ec-4d54-b785-7b9f871ab5e1","resolution":{"observed_at":"2026-05-10T09:23:37.985155Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.10220/citation-record","integrity":"/paper/2412.10220/integrity","json":"/paper/2412.10220/citation-record.json","paper":"/paper/2412.10220"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T16:17:08.104934Z","title":null,"venue":null,"work_id":"d7dfacf4-13c9-44a0-9913-128bddebc800","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.458997Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:b3ee6e25f47f437b4a62fabea7f18292891ee22ecc64ece1d668d313dd17ba0d","observation_id":"66e8265a-312d-4d50-bcfb-756138f8f99a","resolution":{"observed_at":"2026-08-11T16:17:08.108551Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:08.097576Z","title":null,"venue":null,"work_id":"8c41a098-ec9d-47c1-8ccf-d1e0a89b7ee0","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.463646Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:d1a4735e8921e9f607afb636f367f9b8bf92c350457ba45c9a206215b3413159","observation_id":"9369f04f-3c22-4cf1-a56b-7868e9b8e011","resolution":{"observed_at":"2026-08-11T16:17:08.100788Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"4070.0469","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T16:17:07.949843Z","title":"Flemish AI Research Program","venue":null,"work_id":"27dc4bd0-b4ed-4fe0-930e-760ddedc0a7d","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.467290Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:6b3d8191563194844897ceaf1657d835569841b9f99808bac7d31da26ae42fef","observation_id":"100c07a3-073b-4e03-bfc9-2f220f0236f0","resolution":{"observed_at":"2026-08-11T16:17:07.953985Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:08.089945Z","title":"Lundberg and Su-In Lee","venue":null,"work_id":"ec250b03-bcef-4a09-91de-535a638fc08a","year":2017},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.471642Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:8435be4ce010d03816c2736c0f678558271b00d6d13abc449ea3aa788d341d60","observation_id":"cd76681c-7d66-48e2-87c5-d59b7ea23faf","resolution":{"observed_at":"2026-08-11T16:17:08.093224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.475368Z","title":"”why should i trust you?”: Explain- ing the predictions of any classifier","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.475368Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:1a332b04782912b32ce66ac3eb8320ab00cbeb6566282f6eb16c10008d8911fb","observation_id":"066475d5-bf48-49a3-b03e-2192637f8faf","resolution":{"observed_at":"2026-08-11T16:17:07.475368Z","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":"2026-08-11T16:17:07.478794Z","title":"A value for n-person games","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.478794Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e066216a3a2d267be58e6d9c9108a15f576db9019cd58e8bb6e5096577abdca8","observation_id":"b73734ac-5de6-45a1-b424-00d5f4ae1e04","resolution":{"observed_at":"2026-08-11T16:17:07.478794Z","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":"2026-08-11T16:17:08.078339Z","title":"The inadequacy of shapley values for explainabil- ity, 2023","venue":null,"work_id":"ab544a84-ed68-47a4-8e72-2a1f43bf6663","year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.481445Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:17ba96d76180ef74ca4e6972601ce2134dd1e29b4dea10980c159a7ca02c7445","observation_id":"9ced43c6-22ad-4380-9140-5644f9125d11","resolution":{"observed_at":"2026-08-11T16:17:08.081653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.484973Z","title":"Ex- plainability is not a game","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.484973Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:64f1f9c8f5a9a9e2d32b897a1092657eebcdf8a84bc7b7d673408ced9d4fb7bc","observation_id":"9ad79fb9-c267-417e-85d8-edbd5a8324eb","resolution":{"observed_at":"2026-08-11T16:17:07.484973Z","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":"2026-08-11T16:17:07.487993Z","title":"Natural language explanations for machine learning classification decisions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.487993Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:30cbb7027f7cefaa6054162fcf0d1642c1ff1a3c892f7c975ccd62933f2cbcb3","observation_id":"49413a5f-6702-4cb8-91a9-8198cb441cc1","resolution":{"observed_at":"2026-08-11T16:17:07.487993Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.17057","last_updated":"2026-05-27T14:41:49Z","snapshot_observed_at":"2026-07-06T16:25:21.571679Z","submitted_at":"2023-09-29T08:40:08Z","title":"Tell Me a Story! Narrative-Driven XAI with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.17057","snapshot_observed_at":"2026-08-11T16:17:07.492051Z","title":"Tell me a story! narrative-driven xai with large language models, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.492051Z"},"links":{"cited_paper":"/paper/2309.17057","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:60c6325462dedef5fe49a5593764f5db22d2d89d78acac68e68d86eeca2b2129","observation_id":"deceb318-f251-4882-a7e4-c8a7c4ff4d12","resolution":{"observed_at":"2026-08-11T16:17:07.492051Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.06064","last_updated":"2024-05-09T19:17:47Z","snapshot_observed_at":"2026-08-13T00:10:42.644189Z","submitted_at":"2024-05-09T19:17:47Z","title":"LLMs for XAI: Future Directions for Explaining Explanations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.06064","snapshot_observed_at":"2026-08-11T16:17:07.494861Z","title":"Llms for xai: Future directions for explaining explanations, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.494861Z"},"links":{"cited_paper":"/paper/2405.06064","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:9e7619f113f3cd660b040b756f4182a948a8f728d460e3003dcbdcb147a0c819","observation_id":"524364df-b89a-4dfc-ac09-9230b5223d68","resolution":{"observed_at":"2026-08-11T16:17:07.494861Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09295","last_updated":"2025-01-27T13:30:57Z","snapshot_observed_at":"2026-08-12T22:25:01.974777Z","submitted_at":"2024-10-11T23:06:07Z","title":"Natural Language Counterfactual Explanations for Graphs Using Large Language Models","version":2},"cited_work":{"arxiv_id":"2410.09295","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.09295","snapshot_observed_at":"2026-08-11T16:17:07.740217Z","title":"Natural Language Counterfactual Explanations for Graphs Using Large Language Models","venue":"cs.AI","work_id":"5dad1c2c-653b-4411-98c5-e4b4d5f7bd06","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.498907Z"},"links":{"cited_paper":"/paper/2410.09295","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e9585f5942f73722ac7f3ead6333601f2c846e7b5a30de0f27387c10fc88314d","observation_id":"50f871d1-740f-4857-ad95-1f1bcdb6c489","resolution":{"observed_at":"2026-08-11T16:17:07.743925Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.15268","last_updated":"2025-05-28T18:45:19Z","snapshot_observed_at":"2026-08-12T22:19:20.936454Z","submitted_at":"2024-10-20T03:55:46Z","title":"GraphNarrator: Generating Textual Explanations for Graph Neural Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.15268","snapshot_observed_at":"2026-08-11T16:17:07.502476Z","title":"Tagex- plainer: Narrating graph explanations for text- attributed graph learning models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.502476Z"},"links":{"cited_paper":"/paper/2410.15268","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:93ec2cdbece0a01804ae3d335f6105dbdbcc592d1310e6f34927589acc83f0ea","observation_id":"8add35a7-d315-4c68-a1c4-4c6cc4f2ab2d","resolution":{"observed_at":"2026-08-11T16:17:07.502476Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02540","last_updated":"2025-02-12T15:14:01Z","snapshot_observed_at":"2026-08-12T22:07:54.098758Z","submitted_at":"2024-11-04T19:21:06Z","title":"GraphXAIN: Narratives to Explain Graph Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02540","snapshot_observed_at":"2026-08-11T16:17:07.506101Z","title":"Graphx- ain: Narratives to explain graph neural net- works, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.506101Z"},"links":{"cited_paper":"/paper/2411.02540","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:d0ff4cc7ef67339378e12fb42fe0c807a0b8020b3fa93c42e6848a75e3ff3166","observation_id":"bcc5ea74-d2d2-4da0-9243-819377d8c44b","resolution":{"observed_at":"2026-08-11T16:17:07.506101Z","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":"2026-08-11T16:17:07.508986Z","title":"Faithful and plausible natu- ral language explanations for image classifi- cation: A pipeline approach","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.508986Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:d74104ec3dc6f1a4d90e7110a6d5de69d32dc43622272c091f98df5cfca39e16","observation_id":"8d0861c0-51d3-4439-ac24-10a72a6582af","resolution":{"observed_at":"2026-08-11T16:17:07.508986Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05797","last_updated":"2024-07-11T03:42:12Z","snapshot_observed_at":"2026-08-13T05:53:48.058617Z","submitted_at":"2023-10-09T15:31:03Z","title":"In-Context Explainers: Harnessing LLMs for Explaining Black Box Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05797","snapshot_observed_at":"2026-08-11T16:17:07.512632Z","title":"In-context explainers: Harnessing llms for ex- plaining black box models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.512632Z"},"links":{"cited_paper":"/paper/2310.05797","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:95b0737c01c80890834063c1d73397dc92aec4d1fb24738fc5983c93fc26e688","observation_id":"f65c85bd-728f-4e63-a4f9-bae8820dd64e","resolution":{"observed_at":"2026-08-11T16:17:07.512632Z","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":"2026-08-11T16:17:08.071486Z","title":"Explaining ma- chine learning models with interactive natural language conversations using talktomodel","venue":null,"work_id":"77b7e9b9-4837-4650-9f27-0a67ee8e7dea","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.516461Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:063be5b0c4067f45b94096bd076914b8fd9531bff2a1ed517862bbc1ef1c58d7","observation_id":"00ae3619-fd37-4e18-8a74-c1197510ebd9","resolution":{"observed_at":"2026-08-11T16:17:08.074462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:08.055677Z","title":"ME- TEOR: An automatic metric for MT evalu- ation with improved correlation with human judgments","venue":null,"work_id":"625a451a-03b7-4cae-a4e0-d49343128423","year":2005},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.530835Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:f87740c88649fe543705751a23936c19fe3a3fd4060946fb9402814cb1978457","observation_id":"80d6ab0b-8b0c-4aef-875f-12ceea63da2d","resolution":{"observed_at":"2026-08-11T16:17:08.058600Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:08.063174Z","title":"Keane, Eoin M","venue":null,"work_id":"3a88b342-7503-427f-a890-abe3335ee71c","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.521210Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:fc0bd12bf4d18f9f4f1a62636da7324e366f167320bfcd80debb821490b49f52","observation_id":"3460f186-b813-4886-88c3-85778c99a5fa","resolution":{"observed_at":"2026-08-11T16:17:08.066492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:08.047057Z","title":"Do models explain them- selves? Counterfactual simulatability of natural language explanations","venue":null,"work_id":"28b798f5-caac-4101-ae86-f808b068a12c","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.536547Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:5e90518a23edd36387ff339debb1e6eda9e1a6ee9cbe324b83b3c1172d828cf2","observation_id":"2fbded58-85d5-4a66-bd6a-bc8075889d01","resolution":{"observed_at":"2026-08-11T16:17:08.050942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.525849Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.525849Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:fe6690d200c77bde6aa2ef01f991b20c46f5b63ab9fcad40da4557222e31839f","observation_id":"70fdefc5-6b79-40cd-ac59-42c48798b14c","resolution":{"observed_at":"2026-08-11T16:17:07.525849Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T16:17:07.528002Z","title":"BLEURT: Learning robust metrics for text generation","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.528002Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:4188b7b56a881b51f518c783bad94319ed3c0088e883c67b5e783c413c53a128","observation_id":"88041425-3202-4a4e-b038-41cd32b37a42","resolution":{"observed_at":"2026-08-11T16:17:07.528002Z","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":"2026-08-11T16:17:08.030413Z","title":"The disagreement problem in ex- plainable machine learning: A practitioner’s perspective","venue":null,"work_id":"68052b70-6f5f-4194-a419-1caaf6165f91","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.550045Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:b00ea986bbdb91b7fab4b77c1b7d6923ee57716dc9da1ba657dbb88267106693","observation_id":"15b8aa88-629c-4543-ae6c-bbf0700e4d07","resolution":{"observed_at":"2026-08-11T16:17:08.033311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.533968Z","title":"F ActScore: Fine-grained atomic evaluation of factual precision in long form text generation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.533968Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:faa9c7ba26c158e54f3712ada3529df85a865bef726a213dd876a5e76a0c98a8","observation_id":"1403399b-4080-476a-9e3f-8fcbddee5ac0","resolution":{"observed_at":"2026-08-11T16:17:07.533968Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T16:17:08.019232Z","title":"PRobELM: Plausibility ranking evaluation for language models","venue":null,"work_id":"4a2fac65-7072-4282-9bda-7de9f4af333a","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.556439Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:c46bdec2a93f22a68bae9267819e820396bf8d25bcbe959d04d7674e4983a96a","observation_id":"6c835f72-4275-4d2e-b9b4-426295af1f3a","resolution":{"observed_at":"2026-08-11T16:17:08.022375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03993","last_updated":"2024-10-05T09:39:11Z","snapshot_observed_at":"2026-08-13T01:44:48.422178Z","submitted_at":"2024-07-04T15:13:59Z","title":"A Survey on Natural Language Counterfactual Generation","version":2},"cited_work":{"arxiv_id":"2407.03993","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.03993","snapshot_observed_at":"2026-08-11T16:17:07.689049Z","title":"A Survey on Natural Language Counterfactual Generation","venue":"cs.CL","work_id":"b5135a61-04ee-432d-b32c-6c566b645504","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.558746Z"},"links":{"cited_paper":"/paper/2407.03993","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e92a2a572dec0079a2541b9dca7fd77dcf3f437e2d9ccbbce14335dde36ff614","observation_id":"24136dbe-fe04-4b8d-a2a1-a58fbf74c0d6","resolution":{"observed_at":"2026-08-11T16:17:07.693937Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15933","last_updated":"2025-08-03T20:48:55Z","snapshot_observed_at":"2026-07-06T15:47:38.256580Z","submitted_at":"2023-06-28T05:34:25Z","title":"You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting","version":2},"cited_work":{"arxiv_id":"2306.15933","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.15933","snapshot_observed_at":"2026-08-11T16:17:07.706326Z","title":"You Can Generate It Again: Data-to-Text Generation with Verification and Correction Prompting","venue":"cs.CL","work_id":"ce0cb7dd-61cd-41dd-8127-8e22107b73e7","year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.543352Z"},"links":{"cited_paper":"/paper/2306.15933","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:3fd89e680758f81ef3e29a22457ceb95ef65201ccd08289a7a6547326ad75a9a","observation_id":"60bd9c9f-41c1-4e4d-898b-e54c2fdf6162","resolution":{"observed_at":"2026-08-11T16:17:07.709487Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.11584","last_updated":"2024-04-17T17:32:41Z","snapshot_observed_at":"2026-08-04T23:37:27.678120Z","submitted_at":"2024-04-17T17:32:41Z","title":"The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.11584","snapshot_observed_at":"2026-08-11T16:17:07.546973Z","title":"The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.546973Z"},"links":{"cited_paper":"/paper/2404.11584","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:9e22f1f05bea2ebe32c7e2841c4a18b9e0a5d39c0f873fbf1d0afffad1615491","observation_id":"e249ad58-702f-42e6-a1f1-85fb671a1de3","resolution":{"observed_at":"2026-08-11T16:17:07.546973Z","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":"2026-08-11T16:17:08.011195Z","title":"Sentence- bert: Sentence embeddings using siamese bert-networks","venue":null,"work_id":"97a106c0-e1b4-4c30-ab85-5c858969f3d5","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.568624Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:51c044faec1d706ae282657d62094495ed03fd20a652b64445f854984e3050da","observation_id":"2e1eb895-4274-40c0-b4b4-964cfe3e99eb","resolution":{"observed_at":"2026-08-11T16:17:08.014238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.553636Z","title":"Towards few-shot fact-checking via perplexity","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.553636Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:6fb93ab28a9e55a3df0a81323d167bb7a844f4876371324cae55e4214bf14ce0","observation_id":"d1972177-6ae1-4774-bb6c-5862a0a41aeb","resolution":{"observed_at":"2026-08-11T16:17:07.553636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T16:17:07.576150Z","title":"Gpt-4 technical report, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.576150Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:ce62c317f06d4ee3a69cd914bb25229fa005c955a7437641cc54931d077ab5a6","observation_id":"72b38990-3eef-4e12-81ad-7f6e730e9a4a","resolution":{"observed_at":"2026-08-11T16:17:07.576150Z","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":"2026-08-11T16:17:07.996782Z","title":"Claude sonnet 3.5","venue":null,"work_id":"6ec81bc8-8153-4290-a7ef-45cf2580786f","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.580124Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:891a1d5083485a93d5c7673ba2aaddc14688579afd4b6de08a1c1ea17ace27d9","observation_id":"34835850-e086-40d9-aeb6-43a911d25f3a","resolution":{"observed_at":"2026-08-11T16:17:07.999887Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2210.05892","last_updated":"2023-03-15T06:57:46Z","snapshot_observed_at":"2026-07-06T14:03:57.555580Z","submitted_at":"2022-10-12T03:13:28Z","title":"Perplexity from PLM Is Unreliable for Evaluating Text Quality","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.05892","snapshot_observed_at":"2026-08-11T16:17:07.562462Z","title":"Perplexity from plm is unreliable for evaluating text quality, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.562462Z"},"links":{"cited_paper":"/paper/2210.05892","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:8958685bf451f49da02d7ff47d7a656a2ff184b755192d2470ea4e5b785f7696","observation_id":"df8d44f5-21e0-4902-95f6-bcf83eb50d43","resolution":{"observed_at":"2026-08-11T16:17:07.562462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-11T16:17:07.565772Z","title":"Efficient estimation of word representations in vector space, 2013","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.565772Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:a900bbfbb38b364731ec2416c508c5001faee5e62150e417730bb9c85ef0c5ae","observation_id":"da3b3d94-ac13-40f7-8456-cc48b14adc3f","resolution":{"observed_at":"2026-08-11T16:17:07.565772Z","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":"2026-08-11T16:17:07.976274Z","title":"Mistral large 2","venue":null,"work_id":"6d0577fc-5cb0-40f8-b22d-0b10e968a6b8","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.586417Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e4967b08003a9e10a5fa3ad59975d6e9188163aa005eedae2ca44c39a4887b2f","observation_id":"592a9244-b580-4898-aba6-188aca8c656f","resolution":{"observed_at":"2026-08-11T16:17:07.979102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.592213Z","title":"Zhang, Mark Har- man, and Meng Wang","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.592213Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:4c0d7163b5d632a0c4d56b3793e9025780c9b0bbbe63bfa8175d08dd89399de7","observation_id":"e04ac323-90c7-487e-94fc-275ee54e9ca6","resolution":{"observed_at":"2026-08-11T16:17:07.592213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17428","last_updated":"2025-02-25T00:35:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-05-27T17:59:45Z","title":"NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.17428","snapshot_observed_at":"2026-08-11T16:17:07.573421Z","title":"Nv-embed: Im- proved techniques for training llms as gener- alist embedding models, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.573421Z"},"links":{"cited_paper":"/paper/2405.17428","citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:182fa9a37d5a98de2e7deb53848f680a78fc5778ce40aae4cf9ca8137d0c406b","observation_id":"fe4b8619-b339-47a3-b923-d630d7b01e23","resolution":{"observed_at":"2026-08-11T16:17:07.573421Z","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":"2026-08-11T16:17:07.959327Z","title":"Adaptive chameleon or stubborn sloth: Revealing the behavior of large language models in knowledge conflicts","venue":null,"work_id":"41560674-6e5e-4c08-972a-0ef555957019","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.596922Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e5f288817bde4ac75aae89d3db14566953cbdc2bf7198c821d7364a437433fbf","observation_id":"48258627-7921-4712-bed7-593597329f0d","resolution":{"observed_at":"2026-08-11T16:17:07.962454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.599252Z","title":"Context-faithful prompting for large language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.599252Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:815e30f1aa1f053186485fd45c0e350e85c85d877636cdd369a92c2dea8e87f7","observation_id":"e4af75fb-0b5a-4fe1-a1ed-d181f3b854b7","resolution":{"observed_at":"2026-08-11T16:17:07.599252Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T16:17:07.582600Z","title":"Llama 3 model card","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.582600Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:40c567797932069117d44f057974c8cd02c770a72fc132985e329484fa87d17d","observation_id":"82bafdfe-e3be-47da-a95d-ef1fc45eebde","resolution":{"observed_at":"2026-08-11T16:17:07.582600Z","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":"2026-08-11T16:17:07.983915Z","title":"The llama 3 herd of mod- els, 2024","venue":null,"work_id":"a001beb4-c535-4b24-b079-377e58b036a3","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.584489Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:e915d5542cfe824130418923d59fb9bcbe259ae4999474931646401bbd55f0f7","observation_id":"8c428be9-4abb-4c61-a14a-ae9af0b4199e","resolution":{"observed_at":"2026-08-11T16:17:07.987051Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.594559Z","title":"Entity-based knowledge con- flicts in question answering","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.594559Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:d732073657425de3f479fa44c9e7758d39da49b28bb9d325ba68555077a87465","observation_id":"b975b56b-0f68-4a67-a197-23a7941a6e87","resolution":{"observed_at":"2026-08-11T16:17:07.594559Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T16:17:08.004354Z","title":"19 org/CorpusID:201646309","venue":null,"work_id":"be8adc70-50e8-44f7-80de-92cad90f971d","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.571095Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:0afeb7a5d6b82428b13a365d692f8bab647a75e339d2003188c2639b3f55ed94","observation_id":"532d0c77-1923-4217-964a-a52dad5963ae","resolution":{"observed_at":"2026-08-11T16:17:08.006627Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.523664Z","title":"URL https: //doi.org/10.24963/ijcai.2021/609","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.523664Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:d0a11e739b7c4932620980ebb2fc96b4b9e4e946623d6bbd7db45e3785428f11","observation_id":"0c52d410-b766-4a0c-bc5c-1417b54f10fa","resolution":{"observed_at":"2026-08-11T16:17:07.523664Z","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":"2026-08-11T16:17:07.518635Z","title":"doi:10.1038/s42256- 023-00692-8","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.518635Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:2f4009f78fc061f3dc3a4a614f6fffbbbfbf710abd50ed208bc18f492fb478f5","observation_id":"a7508e05-4ea2-42db-9c91-289addbcbf2d","resolution":{"observed_at":"2026-08-11T16:17:07.518635Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-11T16:17:08.037827Z","title":null,"venue":null,"work_id":"bfb8ce0d-d97d-4237-951e-31ba29403689","year":null},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.539365Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:0ea1a42424de623cc8ac449133a153c083c8cb3a302b652540f951abdfc81a1a","observation_id":"9a678ebe-0199-40d4-a588-da6812b0cc67","resolution":{"observed_at":"2026-08-11T16:17:08.041654Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-11T16:17:07.968540Z","title":"URL https://huggingface.co/ mistralai/Mistral-Large-Instruct-2407","venue":null,"work_id":"7e9e745d-8783-4e1a-8d68-b7d2ddd2e33e","year":2024},"citing_paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives","version":1},"reference_index":2407,"source":"pdf_text","source_observed_at":"2026-08-11T16:17:07.589377Z"},"links":{"citing_paper":"/paper/2412.10220"},"observation_digest":"sha256:dcd74c75a5a0bfda6ceaf2118e18563d179a8358d9455306b4893007c8f84465","observation_id":"ca98e4fd-328e-43dc-bcd2-a8cfd1cdd7b3","resolution":{"observed_at":"2026-08-11T16:17:07.971441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.10220","last_updated":"2024-12-13T15:45:45Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T16:10:34.062262Z","submitted_at":"2024-12-13T15:45:45Z","title":"How good is my story? Towards quantitative metrics for evaluating LLM-generated XAI narratives"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":7,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":22,"verified_exact":3,"verified_fuzzy":15},"total_outbound_references":47},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 2 inbound Pith citation observations for arXiv:2412.10220."}