{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CTAC3CSGUQADQD4QOMP3TJREVX","short_pith_number":"pith:CTAC3CSG","schema_version":"1.0","canonical_sha256":"14c02d8a46a400380f90731fb9a624adc3de83fcc8a5f99c8bfa97edc0e82e86","source":{"kind":"arxiv","id":"2312.15398","version":1},"attestation_state":"computed","paper":{"title":"Fairness-Aware Structured Pruning in Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abdelrahman Zayed, Goncalo Mordido, Ioana Baldini, Samira Shabanian, Sarath Chandar","submitted_at":"2023-12-24T03:57:52Z","abstract_excerpt":"The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the large model sizes, however, existing pruning methods solely focus on performance, without considering an essential aspect for the responsible use of LLMs: model fairness. It is crucial to address the fairness of LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish communities, among others, as they are being deployed and available to a wide audience. In this work, first, we investigate how attention"},"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":"2312.15398","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-12-24T03:57:52Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"d62665c4e169bd965da4b8bc67bffa49279e35755f403f45704b2808f32b3bb0","abstract_canon_sha256":"99c91a8d42186953e4769f3849d1bc63581d2479f5273153cce715fc441f1570"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:56.067392Z","signature_b64":"Y4QdXu8hS12b1TA5bAaGdp8jjPX3/8rP6xuite2WFMzfWmOEh/cR8gYsTEc1hK5cm9ttx8ZZjGFYvmpVAgDmBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14c02d8a46a400380f90731fb9a624adc3de83fcc8a5f99c8bfa97edc0e82e86","last_reissued_at":"2026-07-05T07:27:56.066886Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:56.066886Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fairness-Aware Structured Pruning in Transformers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Abdelrahman Zayed, Goncalo Mordido, Ioana Baldini, Samira Shabanian, Sarath Chandar","submitted_at":"2023-12-24T03:57:52Z","abstract_excerpt":"The increasing size of large language models (LLMs) has introduced challenges in their training and inference. Removing model components is perceived as a solution to tackle the large model sizes, however, existing pruning methods solely focus on performance, without considering an essential aspect for the responsible use of LLMs: model fairness. It is crucial to address the fairness of LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish communities, among others, as they are being deployed and available to a wide audience. In this work, first, we investigate how attention"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15398","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/2312.15398/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":"2312.15398","created_at":"2026-07-05T07:27:56.066945+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15398v1","created_at":"2026-07-05T07:27:56.066945+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15398","created_at":"2026-07-05T07:27:56.066945+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTAC3CSGUQAD","created_at":"2026-07-05T07:27:56.066945+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTAC3CSGUQADQD4Q","created_at":"2026-07-05T07:27:56.066945+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTAC3CSG","created_at":"2026-07-05T07:27:56.066945+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX","json":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX.json","graph_json":"https://pith.science/api/pith-number/CTAC3CSGUQADQD4QOMP3TJREVX/graph.json","events_json":"https://pith.science/api/pith-number/CTAC3CSGUQADQD4QOMP3TJREVX/events.json","paper":"https://pith.science/paper/CTAC3CSG"},"agent_actions":{"view_html":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX","download_json":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX.json","view_paper":"https://pith.science/paper/CTAC3CSG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15398&json=true","fetch_graph":"https://pith.science/api/pith-number/CTAC3CSGUQADQD4QOMP3TJREVX/graph.json","fetch_events":"https://pith.science/api/pith-number/CTAC3CSGUQADQD4QOMP3TJREVX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX/action/storage_attestation","attest_author":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX/action/author_attestation","sign_citation":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX/action/citation_signature","submit_replication":"https://pith.science/pith/CTAC3CSGUQADQD4QOMP3TJREVX/action/replication_record"}},"created_at":"2026-07-05T07:27:56.066945+00:00","updated_at":"2026-07-05T07:27:56.066945+00:00"}