{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VMHXS2KHFQOIOCSSR3PU3IOL6Q","short_pith_number":"pith:VMHXS2KH","schema_version":"1.0","canonical_sha256":"ab0f7969472c1c870a528edf4da1cbf42b722ee52e1a67f2bf429ff4cb1210cb","source":{"kind":"arxiv","id":"2109.03646","version":1},"attestation_state":"computed","paper":{"title":"Sustainable Modular Debiasing of Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anne Lauscher, Goran Glava\\v{s}, Tobias L\\\"uken","submitted_at":"2021-09-08T13:42:34Z","abstract_excerpt":"Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which -- besides being computationally expensive -- comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretrainin"},"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":"2109.03646","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2021-09-08T13:42:34Z","cross_cats_sorted":[],"title_canon_sha256":"231067b67e6ddf016b17be6c83afb40f420ab17d1e0ba58aea539c67f7f45094","abstract_canon_sha256":"2f85ec9c8867c284344b38e0de837832fb9a83ab331547f6c1dc32660dbd862a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:12:41.577923Z","signature_b64":"lLVjkqUHNHvyVWzP6TNU7bU99qDfB6G4vLoxY1PZmrT4g1KrJT4ZZh5CAfZtu0piEgxg8JkQ2JrTN2cThoAnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab0f7969472c1c870a528edf4da1cbf42b722ee52e1a67f2bf429ff4cb1210cb","last_reissued_at":"2026-07-05T03:12:41.577428Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:12:41.577428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sustainable Modular Debiasing of Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anne Lauscher, Goran Glava\\v{s}, Tobias L\\\"uken","submitted_at":"2021-09-08T13:42:34Z","abstract_excerpt":"Unfair stereotypical biases (e.g., gender, racial, or religious biases) encoded in modern pretrained language models (PLMs) have negative ethical implications for widespread adoption of state-of-the-art language technology. To remedy for this, a wide range of debiasing techniques have recently been introduced to remove such stereotypical biases from PLMs. Existing debiasing methods, however, directly modify all of the PLMs parameters, which -- besides being computationally expensive -- comes with the inherent risk of (catastrophic) forgetting of useful language knowledge acquired in pretrainin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2109.03646","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/2109.03646/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":"2109.03646","created_at":"2026-07-05T03:12:41.577489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2109.03646v1","created_at":"2026-07-05T03:12:41.577489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2109.03646","created_at":"2026-07-05T03:12:41.577489+00:00"},{"alias_kind":"pith_short_12","alias_value":"VMHXS2KHFQOI","created_at":"2026-07-05T03:12:41.577489+00:00"},{"alias_kind":"pith_short_16","alias_value":"VMHXS2KHFQOIOCSS","created_at":"2026-07-05T03:12:41.577489+00:00"},{"alias_kind":"pith_short_8","alias_value":"VMHXS2KH","created_at":"2026-07-05T03:12:41.577489+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.15475","citing_title":"LFTF: Locating First and Then Fine-Tuning for Mitigating Gender Bias in Large Language Models","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q","json":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q.json","graph_json":"https://pith.science/api/pith-number/VMHXS2KHFQOIOCSSR3PU3IOL6Q/graph.json","events_json":"https://pith.science/api/pith-number/VMHXS2KHFQOIOCSSR3PU3IOL6Q/events.json","paper":"https://pith.science/paper/VMHXS2KH"},"agent_actions":{"view_html":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q","download_json":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q.json","view_paper":"https://pith.science/paper/VMHXS2KH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2109.03646&json=true","fetch_graph":"https://pith.science/api/pith-number/VMHXS2KHFQOIOCSSR3PU3IOL6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/VMHXS2KHFQOIOCSSR3PU3IOL6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q/action/storage_attestation","attest_author":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q/action/author_attestation","sign_citation":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q/action/citation_signature","submit_replication":"https://pith.science/pith/VMHXS2KHFQOIOCSSR3PU3IOL6Q/action/replication_record"}},"created_at":"2026-07-05T03:12:41.577489+00:00","updated_at":"2026-07-05T03:12:41.577489+00:00"}