{"as_of":"2026-08-08T01:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:894ecfc8cbd6bf9e82257aac7bd052efe4bf6fdb18b835428d48946b73ecb3b2","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:30:41.895329Z","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":"2026-06-28T23:02:46.594391Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-07T15:30:41.895329Z","title":"Disclosure and mitigation of gender bias in llms.arXiv preprint arXiv:2402.11190,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.14971","last_updated":"2025-06-05T00:42:04Z","snapshot_observed_at":"2026-08-07T23:45:16.588145Z","submitted_at":"2025-05-20T23:19:13Z","title":"DECASTE: Unveiling Caste Stereotypes in Large Language Models through Multi-Dimensional Bias Analysis","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T15:30:41.895329Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2505.14971"},"observation_digest":"sha256:edb824106834094eb80e3d49e3e701268264748598992a6c7d79f0f31ad984e5","observation_id":"c53f09e7-b6fb-4bf8-948a-542f0131925c","resolution":{"observed_at":"2026-08-07T15:30:41.895329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-07T15:22:12.962995Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.15475","last_updated":"2025-05-21T12:49:37Z","snapshot_observed_at":"2026-08-07T23:55:52.042948Z","submitted_at":"2025-05-21T12:49:37Z","title":"LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T15:22:12.962995Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2505.15475"},"observation_digest":"sha256:e2c5dcb231cbf3b3aa671e6e57c63ea39665766c114d8d39007fb197042aefad","observation_id":"0853d23c-38b0-476a-89dd-2fdcb99cfa6f","resolution":{"observed_at":"2026-08-07T15:22:12.962995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-07T15:12:19.415481Z","title":"Disclosure and mitigation of gender bias in llms","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.17131","last_updated":"2025-05-22T01:59:54Z","snapshot_observed_at":"2026-08-07T23:47:01.265288Z","submitted_at":"2025-05-22T01:59:54Z","title":"Relative Bias: A Comparative Framework for Quantifying Bias in LLMs","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:12:19.415481Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2505.17131"},"observation_digest":"sha256:7fb6db31ec1e63a875f4fda55fb9423daa096fa92d266a7382ab27756db5a446","observation_id":"8157e21b-4b7c-4a7f-a39b-ebe9562fb848","resolution":{"observed_at":"2026-08-07T15:12:19.415481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-07T00:50:56.057286Z","title":"arXiv preprint arXiv:2402.11190 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.12574","last_updated":"2025-06-14T17:06:04Z","snapshot_observed_at":"2026-08-07T23:46:40.035870Z","submitted_at":"2025-06-14T17:06:04Z","title":"Overview of the NLPCC 2025 Shared Task: Gender Bias Mitigation Challenge","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T00:50:56.057286Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2506.12574"},"observation_digest":"sha256:abe1fef4eb381a0285a752d7de593e2f34033074691f13329cd35a9c00dfef32","observation_id":"a8b46a31-0180-4692-9439-1e938cb7a5c3","resolution":{"observed_at":"2026-08-07T00:50:56.057286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-06T23:59:58.058321Z","title":"Yu, and James Caverlee","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.15568","last_updated":"2025-06-18T15:43:16Z","snapshot_observed_at":"2026-08-06T23:50:35.774924Z","submitted_at":"2025-06-18T15:43:16Z","title":"Gender Inclusivity Fairness Index (GIFI): A Multilevel Framework for Evaluating Gender Diversity in Large Language Models","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T23:59:58.058321Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2506.15568"},"observation_digest":"sha256:68e754b721318a567db74b0a9719124cb031c69b34002366dfbc5f6274c28f5d","observation_id":"a9e3678b-4cdf-472a-9609-8dc179339c84","resolution":{"observed_at":"2026-08-06T23:59:58.058321Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-05T10:16:43.138825Z","title":"Yu, and James Caverlee","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.04373","last_updated":"2025-09-10T06:08:26Z","snapshot_observed_at":"2026-08-08T00:18:57.626136Z","submitted_at":"2025-09-04T16:32:18Z","title":"Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T10:16:43.138825Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2509.04373"},"observation_digest":"sha256:5a396fb34ab1f993b917020f419cd5f4ac0f8d56470255e063d0bce972e0b670","observation_id":"f1f0a0c1-cedd-4bfb-a948-114f1bc6562a","resolution":{"observed_at":"2026-08-05T10:16:43.138825Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":"2402.11190","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-06-28T23:02:46.594391Z","title":"Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A","venue":null,"work_id":"f55d5fc8-4d2d-4abf-a3ef-9876429b8b75","year":2025},"citing_paper":{"arxiv_id":"2511.08484","last_updated":"2026-04-27T17:07:15Z","snapshot_observed_at":"2026-08-07T17:50:48.937259Z","submitted_at":"2025-11-11T17:25:44Z","title":"Patching LLM Like Software: A Lightweight Method for Improving Safety Policy in Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-17T23:24:40.372674Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2511.08484"},"observation_digest":"sha256:985a1b6d28095cfc7f00398827e0912b0459e9c3a0b7771be8d98c00a8fd8b8a","observation_id":"5ce9cee3-824c-4b20-99c6-def5d04e8d37","resolution":{"observed_at":"2026-05-17T23:25:28.501024Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-03T04:43:42.239692Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2602.04306","last_updated":"2026-06-18T06:47:05Z","snapshot_observed_at":"2026-08-07T23:47:21.727867Z","submitted_at":"2026-02-04T08:15:51Z","title":"DeFrame: Debiasing Large Language Models Against Framing Effects","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-03T04:43:42.239692Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2602.04306"},"observation_digest":"sha256:cae1987ae96e57f401b567fdb77ac063b7a590c27721d67f4cf3d5ca3d005810","observation_id":"490ad397-6d8a-4abf-b179-f5ec35d4c828","resolution":{"observed_at":"2026-08-03T04:43:42.239692Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":"2402.11190","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-06-28T23:02:46.594391Z","title":"Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A","venue":null,"work_id":"f55d5fc8-4d2d-4abf-a3ef-9876429b8b75","year":2025},"citing_paper":{"arxiv_id":"2605.30717","last_updated":"2026-05-29T01:21:40Z","snapshot_observed_at":"2026-07-29T22:38:02.826956Z","submitted_at":"2026-05-29T01:21:40Z","title":"Neuron-Level Interventions for Gendered and Gender-Neutral Generation in Language Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T22:58:30.529144Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2605.30717"},"observation_digest":"sha256:54a86bb997bf6c8878434593528ff88f2414842205720970cb9930137f523480","observation_id":"064c96d7-8c2e-4275-ae01-41b72e181065","resolution":{"observed_at":"2026-06-28T23:02:46.596063Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11190","snapshot_observed_at":"2026-08-05T15:51:34.787917Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.03627","last_updated":"2026-08-04T13:14:06Z","snapshot_observed_at":"2026-08-07T23:12:12.303774Z","submitted_at":"2026-08-04T13:14:06Z","title":"Unequal Verdicts: Investigating Gender Bias in LLM-Based Fake News Detection","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T15:51:34.787917Z"},"links":{"cited_paper":"/paper/2402.11190","citing_paper":"/paper/2608.03627"},"observation_digest":"sha256:249f7be59559c4ed61ff428167fdfb30834f28a5119e7d7bfa7e50f758826560","observation_id":"982d0cb8-a892-45dd-8e38-b04c07f1135a","resolution":{"observed_at":"2026-08-05T15:51:34.787917Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2402.11190/citation-record","integrity":"/paper/2402.11190/integrity","json":"/paper/2402.11190/citation-record.json","paper":"/paper/2402.11190"},"outbound":[],"paper":{"arxiv_id":"2402.11190","last_updated":"2024-02-17T04:48:55Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-03T04:47:47.300932Z","submitted_at":"2024-02-17T04:48:55Z","title":"Disclosure and Mitigation of Gender Bias in LLMs"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2402.11190."}