{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:W35TDWA4JEDWSMTR5BCO3VK3NN","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3b5320b358b7c019d0b1a78140977e89284bf993c5df44020478f915e27a8cb8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T09:43:36Z","title_canon_sha256":"41d4e04a3aed75e00f523560af3838f4b189c164ee0d994a3941364a546d98fc"},"schema_version":"1.0","source":{"id":"2502.11603","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.11603","created_at":"2026-07-05T10:15:31Z"},{"alias_kind":"arxiv_version","alias_value":"2502.11603v1","created_at":"2026-07-05T10:15:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.11603","created_at":"2026-07-05T10:15:31Z"},{"alias_kind":"pith_short_12","alias_value":"W35TDWA4JEDW","created_at":"2026-07-05T10:15:31Z"},{"alias_kind":"pith_short_16","alias_value":"W35TDWA4JEDWSMTR","created_at":"2026-07-05T10:15:31Z"},{"alias_kind":"pith_short_8","alias_value":"W35TDWA4","created_at":"2026-07-05T10:15:31Z"}],"graph_snapshots":[{"event_id":"sha256:5dec9595a7003dfffd627377652d0f621627d79f203448ebae9c32baea06fa82","target":"graph","created_at":"2026-07-05T10:15:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.11603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) exhibit strong natural language processing capabilities but also inherit and amplify societal biases, including gender bias, raising fairness concerns. Existing debiasing methods face significant limitations: parameter tuning requires access to model weights, prompt-based approaches often degrade model utility, and optimization-based techniques lack generalizability. To address these challenges, we propose DR.GAP (Demonstration and Reasoning for Gender-Aware Prompting), an automated and model-agnostic approach that mitigates gender bias while preserving model perfo","authors_text":"Hongye Qiu, Meikang Qiu, Wenjie Wang, Yue Xu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T09:43:36Z","title":"DR.GAP: Mitigating Bias in Large Language Models using Gender-Aware Prompting with Demonstration and Reasoning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.11603","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:97489af07c2d818d6e9c04b06399b138e0dbcdf920acc57867257343ce4fee26","target":"record","created_at":"2026-07-05T10:15:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3b5320b358b7c019d0b1a78140977e89284bf993c5df44020478f915e27a8cb8","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-02-17T09:43:36Z","title_canon_sha256":"41d4e04a3aed75e00f523560af3838f4b189c164ee0d994a3941364a546d98fc"},"schema_version":"1.0","source":{"id":"2502.11603","kind":"arxiv","version":1}},"canonical_sha256":"b6fb31d81c4907693271e844edd55b6b58f7d72079fefc0a0d40bad587c5cddf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b6fb31d81c4907693271e844edd55b6b58f7d72079fefc0a0d40bad587c5cddf","first_computed_at":"2026-07-05T10:15:31.436913Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:15:31.436913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i8weJwxJoemUfhQLL1CLj0CbHOybmtlPQHYw8O4brifNQwgdhaLbPP7DxCfKJ9qWSldDhB5ssPZCkaAkR+o0Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T10:15:31.437404Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.11603","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:97489af07c2d818d6e9c04b06399b138e0dbcdf920acc57867257343ce4fee26","sha256:5dec9595a7003dfffd627377652d0f621627d79f203448ebae9c32baea06fa82"],"state_sha256":"9a8442ce012d18c83e34c0d0e64757bf23c11e1d9ff8a05b3b384e27e964cc08"}