{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AHGFFBBUFQCJW4IH5QIOXSHPR5","short_pith_number":"pith:AHGFFBBU","schema_version":"1.0","canonical_sha256":"01cc5284342c049b7107ec10ebc8ef8f76e2d983902a57c4df528069c4123895","source":{"kind":"arxiv","id":"2506.12527","version":1},"attestation_state":"computed","paper":{"title":"Detection, Classification, and Mitigation of Gender Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hongying Zan, Jinwang Song, Lulu Kong, Min Peng, Xiaoqing Cheng","submitted_at":"2025-06-14T14:53:25Z","abstract_excerpt":"With the rapid development of large language models (LLMs), they have significantly improved efficiency across a wide range of domains. However, recent studies have revealed that LLMs often exhibit gender bias, leading to serious social implications. Detecting, classifying, and mitigating gender bias in LLMs has therefore become a critical research focus. In the NLPCC 2025 Shared Task 7: Chinese Corpus for Gender Bias Detection, Classification and Mitigation Challenge, we investigate how to enhance the capabilities of LLMs in gender bias detection, classification, and mitigation. We adopt rein"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2506.12527","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-14T14:53:25Z","cross_cats_sorted":[],"title_canon_sha256":"edaa0f6d45cb8adc290cc4a7195415c28b885cea33ba5e70d84d9483a1d11dc8","abstract_canon_sha256":"e658c0290698f7a7db88f9505123264c6d137276d59a5b691d8bb2c366228014"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:54.773217Z","signature_b64":"cNGQVzwofLgeOgnBUbeMxbsYyqytWTYoCKCXZTFfj/pMIgkh8XjZND4cfoPZc8Wy5oGIQ/ilgFceyNU4r/LhAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01cc5284342c049b7107ec10ebc8ef8f76e2d983902a57c4df528069c4123895","last_reissued_at":"2026-07-05T11:21:54.772693Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:54.772693Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detection, Classification, and Mitigation of Gender Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hongying Zan, Jinwang Song, Lulu Kong, Min Peng, Xiaoqing Cheng","submitted_at":"2025-06-14T14:53:25Z","abstract_excerpt":"With the rapid development of large language models (LLMs), they have significantly improved efficiency across a wide range of domains. However, recent studies have revealed that LLMs often exhibit gender bias, leading to serious social implications. Detecting, classifying, and mitigating gender bias in LLMs has therefore become a critical research focus. In the NLPCC 2025 Shared Task 7: Chinese Corpus for Gender Bias Detection, Classification and Mitigation Challenge, we investigate how to enhance the capabilities of LLMs in gender bias detection, classification, and mitigation. We adopt rein"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12527","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2506.12527/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.12527","created_at":"2026-07-05T11:21:54.772745+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12527v1","created_at":"2026-07-05T11:21:54.772745+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12527","created_at":"2026-07-05T11:21:54.772745+00:00"},{"alias_kind":"pith_short_12","alias_value":"AHGFFBBUFQCJ","created_at":"2026-07-05T11:21:54.772745+00:00"},{"alias_kind":"pith_short_16","alias_value":"AHGFFBBUFQCJW4IH","created_at":"2026-07-05T11:21:54.772745+00:00"},{"alias_kind":"pith_short_8","alias_value":"AHGFFBBU","created_at":"2026-07-05T11:21:54.772745+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.03871","citing_title":"A Comprehensive Survey on Trustworthiness in Reasoning with Large Language Models","ref_index":207,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5","json":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5.json","graph_json":"https://pith.science/api/pith-number/AHGFFBBUFQCJW4IH5QIOXSHPR5/graph.json","events_json":"https://pith.science/api/pith-number/AHGFFBBUFQCJW4IH5QIOXSHPR5/events.json","paper":"https://pith.science/paper/AHGFFBBU"},"agent_actions":{"view_html":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5","download_json":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5.json","view_paper":"https://pith.science/paper/AHGFFBBU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12527&json=true","fetch_graph":"https://pith.science/api/pith-number/AHGFFBBUFQCJW4IH5QIOXSHPR5/graph.json","fetch_events":"https://pith.science/api/pith-number/AHGFFBBUFQCJW4IH5QIOXSHPR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5/action/storage_attestation","attest_author":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5/action/author_attestation","sign_citation":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5/action/citation_signature","submit_replication":"https://pith.science/pith/AHGFFBBUFQCJW4IH5QIOXSHPR5/action/replication_record"}},"created_at":"2026-07-05T11:21:54.772745+00:00","updated_at":"2026-07-05T11:21:54.772745+00:00"}