{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:AGVVVHYGL4TSFRQC5YKRDDFMVX","short_pith_number":"pith:AGVVVHYG","schema_version":"1.0","canonical_sha256":"01ab5a9f065f2722c602ee15118cacade8b4d78510ee8d7bfce1ca483eb3e3c8","source":{"kind":"arxiv","id":"2310.09219","version":5},"attestation_state":"computed","paper":{"title":"\"Kelly is a Warm Person, Joseph is a Role Model\": Gender Biases in LLM-Generated Reference Letters","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aparna Garimella, George Pu, Jiao Sun, Kai-Wei Chang, Nanyun Peng, Yixin Wan","submitted_at":"2023-10-13T16:12:57Z","abstract_excerpt":"Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content, including professional documents such as recommendation letters. Though bringing convenience, this application also introduces unprecedented fairness concerns. Model-generated reference letters might be directly used by users in professional scenarios. If underlying biases exist in these model-constructed letters, using them without scrutinization could lead to direct societal harms, such as sabotaging application success rates for female applicants. In light of th"},"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":"2310.09219","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-13T16:12:57Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cec3459c278bff01f4a154fa5ccf4b1ada3215e403bad64c98ae191b83fcf26d","abstract_canon_sha256":"79a33b484ff54d107a8e56c1b2910ddbdb7410027266f038d888fe417fde7070"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:19:23.131945Z","signature_b64":"N7SawGWsRJKUtLNVY+NBJHpte6PEbmyZXsH6J/BLxcMB9rY+VqHCMKfLkH0/imxyWIoRsaBFV6IiUmnxH55/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01ab5a9f065f2722c602ee15118cacade8b4d78510ee8d7bfce1ca483eb3e3c8","last_reissued_at":"2026-07-05T07:19:23.131426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:19:23.131426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"\"Kelly is a Warm Person, Joseph is a Role Model\": Gender Biases in LLM-Generated Reference Letters","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aparna Garimella, George Pu, Jiao Sun, Kai-Wei Chang, Nanyun Peng, Yixin Wan","submitted_at":"2023-10-13T16:12:57Z","abstract_excerpt":"Large Language Models (LLMs) have recently emerged as an effective tool to assist individuals in writing various types of content, including professional documents such as recommendation letters. Though bringing convenience, this application also introduces unprecedented fairness concerns. Model-generated reference letters might be directly used by users in professional scenarios. If underlying biases exist in these model-constructed letters, using them without scrutinization could lead to direct societal harms, such as sabotaging application success rates for female applicants. In light of th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09219","kind":"arxiv","version":5},"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/2310.09219/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":"2310.09219","created_at":"2026-07-05T07:19:23.131490+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.09219v5","created_at":"2026-07-05T07:19:23.131490+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09219","created_at":"2026-07-05T07:19:23.131490+00:00"},{"alias_kind":"pith_short_12","alias_value":"AGVVVHYGL4TS","created_at":"2026-07-05T07:19:23.131490+00:00"},{"alias_kind":"pith_short_16","alias_value":"AGVVVHYGL4TSFRQC","created_at":"2026-07-05T07:19:23.131490+00:00"},{"alias_kind":"pith_short_8","alias_value":"AGVVVHYG","created_at":"2026-07-05T07:19:23.131490+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2504.15801","citing_title":"A closer look at how large language models trust humans: patterns and biases","ref_index":32,"is_internal_anchor":false},{"citing_arxiv_id":"2508.06649","citing_title":"Measuring Stereotype and Deviation Biases in Large Language Models","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2509.13400","citing_title":"Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews","ref_index":20,"is_internal_anchor":false},{"citing_arxiv_id":"2401.05561","citing_title":"TrustLLM: Trustworthiness in Large Language Models","ref_index":262,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14672","citing_title":"SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models","ref_index":63,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX","json":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX.json","graph_json":"https://pith.science/api/pith-number/AGVVVHYGL4TSFRQC5YKRDDFMVX/graph.json","events_json":"https://pith.science/api/pith-number/AGVVVHYGL4TSFRQC5YKRDDFMVX/events.json","paper":"https://pith.science/paper/AGVVVHYG"},"agent_actions":{"view_html":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX","download_json":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX.json","view_paper":"https://pith.science/paper/AGVVVHYG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.09219&json=true","fetch_graph":"https://pith.science/api/pith-number/AGVVVHYGL4TSFRQC5YKRDDFMVX/graph.json","fetch_events":"https://pith.science/api/pith-number/AGVVVHYGL4TSFRQC5YKRDDFMVX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX/action/storage_attestation","attest_author":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX/action/author_attestation","sign_citation":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX/action/citation_signature","submit_replication":"https://pith.science/pith/AGVVVHYGL4TSFRQC5YKRDDFMVX/action/replication_record"}},"created_at":"2026-07-05T07:19:23.131490+00:00","updated_at":"2026-07-05T07:19:23.131490+00:00"}