{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:TMNAWJR44W4HRCSJC3XMRGBGWH","short_pith_number":"pith:TMNAWJR4","schema_version":"1.0","canonical_sha256":"9b1a0b263ce5b8788a4916eec89826b1ecdd4057734505429b730e479f907f5e","source":{"kind":"arxiv","id":"2412.16451","version":1},"attestation_state":"computed","paper":{"title":"Correcting Large Language Model Behavior via Influence Function","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Bin Liang, Hanyang Peng, Han Zhang, Hui Wang, Lin Gui, Ruifeng Xu, Yi Zhang, Yuanzhao Zhai, Yue Yu, Yu Lei, Zhuo Zhang","submitted_at":"2024-12-21T02:50:08Z","abstract_excerpt":"Recent advancements in AI alignment techniques have significantly improved the alignment of large language models (LLMs) with static human preferences. However, the dynamic nature of human preferences can render some prior training data outdated or even erroneous, ultimately causing LLMs to deviate from contemporary human preferences and societal norms. Existing methodologies, whether they involve the curation of new data for continual alignment or the manual correction of outdated data for re-alignment, demand costly human resources. To address this challenge, we propose a novel approach, Lar"},"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":"2412.16451","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-21T02:50:08Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"b41a6f8c75dbb60c61ad5e9ebed500e9407e16429d3786be15dce44a06ea3da6","abstract_canon_sha256":"47496c9e8176a421d3bead87b8e330a7c33a4c5f5bfdf7760f227408e2877161"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:46.326661Z","signature_b64":"wPxKn0g8HTQzXnsRD4NpPA3HL86oiB5uhHJ6Cg0w1h8JYaHDXFanqBA1q3ibNq2B9uAyMWyT/k9DcyznUX/VDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b1a0b263ce5b8788a4916eec89826b1ecdd4057734505429b730e479f907f5e","last_reissued_at":"2026-07-05T09:52:46.326114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:46.326114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Correcting Large Language Model Behavior via Influence Function","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Bin Liang, Hanyang Peng, Han Zhang, Hui Wang, Lin Gui, Ruifeng Xu, Yi Zhang, Yuanzhao Zhai, Yue Yu, Yu Lei, Zhuo Zhang","submitted_at":"2024-12-21T02:50:08Z","abstract_excerpt":"Recent advancements in AI alignment techniques have significantly improved the alignment of large language models (LLMs) with static human preferences. However, the dynamic nature of human preferences can render some prior training data outdated or even erroneous, ultimately causing LLMs to deviate from contemporary human preferences and societal norms. Existing methodologies, whether they involve the curation of new data for continual alignment or the manual correction of outdated data for re-alignment, demand costly human resources. To address this challenge, we propose a novel approach, Lar"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16451","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/2412.16451/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":"2412.16451","created_at":"2026-07-05T09:52:46.326178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.16451v1","created_at":"2026-07-05T09:52:46.326178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16451","created_at":"2026-07-05T09:52:46.326178+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMNAWJR44W4H","created_at":"2026-07-05T09:52:46.326178+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMNAWJR44W4HRCSJ","created_at":"2026-07-05T09:52:46.326178+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMNAWJR4","created_at":"2026-07-05T09:52:46.326178+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/TMNAWJR44W4HRCSJC3XMRGBGWH","json":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH.json","graph_json":"https://pith.science/api/pith-number/TMNAWJR44W4HRCSJC3XMRGBGWH/graph.json","events_json":"https://pith.science/api/pith-number/TMNAWJR44W4HRCSJC3XMRGBGWH/events.json","paper":"https://pith.science/paper/TMNAWJR4"},"agent_actions":{"view_html":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH","download_json":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH.json","view_paper":"https://pith.science/paper/TMNAWJR4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.16451&json=true","fetch_graph":"https://pith.science/api/pith-number/TMNAWJR44W4HRCSJC3XMRGBGWH/graph.json","fetch_events":"https://pith.science/api/pith-number/TMNAWJR44W4HRCSJC3XMRGBGWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH/action/storage_attestation","attest_author":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH/action/author_attestation","sign_citation":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH/action/citation_signature","submit_replication":"https://pith.science/pith/TMNAWJR44W4HRCSJC3XMRGBGWH/action/replication_record"}},"created_at":"2026-07-05T09:52:46.326178+00:00","updated_at":"2026-07-05T09:52:46.326178+00:00"}