{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WNNLFABNNEV2UTOHRVDWUBO3JY","short_pith_number":"pith:WNNLFABN","schema_version":"1.0","canonical_sha256":"b35ab2802d692baa4dc78d476a05db4e3b4542dbd8d3c73977df7954b582b2d2","source":{"kind":"arxiv","id":"2410.11084","version":1},"attestation_state":"computed","paper":{"title":"Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mark Dredze, Michelle R. Kaufman, Sharon Levy, Tahilin Sanchez Karver, William D. Adler","submitted_at":"2024-10-14T20:50:11Z","abstract_excerpt":"Large language models (LLMs) acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. Prior studies have demonstrated model generations favor one gender or exhibit stereotypes about gender, but have not investigated the complex dynamics that can influence model reasoning and decision-making involving gender. We study gender equity within LLMs through a decision-making lens with a new dataset, DeMET Prompts, containing scenarios related to intimate, romantic relationships. We explore nine relationship configurations through name pairs "},"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":"2410.11084","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-14T20:50:11Z","cross_cats_sorted":[],"title_canon_sha256":"108a546e7a5f8af9aa924f8e671e662c162de1bc986215f4143bf11d132a801c","abstract_canon_sha256":"5bd6217cc3a6868cb0f27c1db5161431151f920da2253932435a15fb26f520e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:28.976069Z","signature_b64":"7Af9C8F+tdQ/51isVecf2BFK+b54qc1BYKILTrZjeOoFdUlsUJWaH8CePhcI2AaaDj5d05abIQ57H9R9cZ+XAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b35ab2802d692baa4dc78d476a05db4e3b4542dbd8d3c73977df7954b582b2d2","last_reissued_at":"2026-07-05T09:20:28.975610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:28.975610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Mark Dredze, Michelle R. Kaufman, Sharon Levy, Tahilin Sanchez Karver, William D. Adler","submitted_at":"2024-10-14T20:50:11Z","abstract_excerpt":"Large language models (LLMs) acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. Prior studies have demonstrated model generations favor one gender or exhibit stereotypes about gender, but have not investigated the complex dynamics that can influence model reasoning and decision-making involving gender. We study gender equity within LLMs through a decision-making lens with a new dataset, DeMET Prompts, containing scenarios related to intimate, romantic relationships. We explore nine relationship configurations through name pairs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.11084","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/2410.11084/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":"2410.11084","created_at":"2026-07-05T09:20:28.975674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.11084v1","created_at":"2026-07-05T09:20:28.975674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.11084","created_at":"2026-07-05T09:20:28.975674+00:00"},{"alias_kind":"pith_short_12","alias_value":"WNNLFABNNEV2","created_at":"2026-07-05T09:20:28.975674+00:00"},{"alias_kind":"pith_short_16","alias_value":"WNNLFABNNEV2UTOH","created_at":"2026-07-05T09:20:28.975674+00:00"},{"alias_kind":"pith_short_8","alias_value":"WNNLFABN","created_at":"2026-07-05T09:20:28.975674+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23101","citing_title":"From Individuals to Interactions: Benchmarking Gender Bias in Multimodal Large Language Models from the Lens of Social Relationship","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY","json":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY.json","graph_json":"https://pith.science/api/pith-number/WNNLFABNNEV2UTOHRVDWUBO3JY/graph.json","events_json":"https://pith.science/api/pith-number/WNNLFABNNEV2UTOHRVDWUBO3JY/events.json","paper":"https://pith.science/paper/WNNLFABN"},"agent_actions":{"view_html":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY","download_json":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY.json","view_paper":"https://pith.science/paper/WNNLFABN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.11084&json=true","fetch_graph":"https://pith.science/api/pith-number/WNNLFABNNEV2UTOHRVDWUBO3JY/graph.json","fetch_events":"https://pith.science/api/pith-number/WNNLFABNNEV2UTOHRVDWUBO3JY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY/action/storage_attestation","attest_author":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY/action/author_attestation","sign_citation":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY/action/citation_signature","submit_replication":"https://pith.science/pith/WNNLFABNNEV2UTOHRVDWUBO3JY/action/replication_record"}},"created_at":"2026-07-05T09:20:28.975674+00:00","updated_at":"2026-07-05T09:20:28.975674+00:00"}