{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7TWQNAJ75CLKPW6CPBUIGBAI7R","short_pith_number":"pith:7TWQNAJ7","schema_version":"1.0","canonical_sha256":"fced06813fe896a7dbc27868830408fc6f0d3a71464412842c06da67cc4076c2","source":{"kind":"arxiv","id":"2401.10768","version":5},"attestation_state":"computed","paper":{"title":"Knowledge Verification to Nip Hallucination in the Bud","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fanqi Wan, Leyang Cui, Shuming Shi, Wei Bi, Xiaojun Quan, Xinting Huang","submitted_at":"2024-01-19T15:39:49Z","abstract_excerpt":"While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. In this paper, we demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. Specifically, we propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-al"},"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":"2401.10768","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-19T15:39:49Z","cross_cats_sorted":[],"title_canon_sha256":"0ca0092ef9b73db557ff22786558521ec09fff203dd9f4514fafa5244d5d540f","abstract_canon_sha256":"f30f4bda7cd92a1f1f8a586ee00ac91eba1975f19d4a2bcb16caa1c76a4b1e6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:09:55.638012Z","signature_b64":"/CYknhKKAQ/SjGXNYEV/TEfK9oaQXV4DupTwnygCpWhdPYWsOX7Gf/MjYpyntZnDtxNrME+xqqeDi2ooVensCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fced06813fe896a7dbc27868830408fc6f0d3a71464412842c06da67cc4076c2","last_reissued_at":"2026-07-05T09:09:55.637553Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:09:55.637553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Knowledge Verification to Nip Hallucination in the Bud","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fanqi Wan, Leyang Cui, Shuming Shi, Wei Bi, Xiaojun Quan, Xinting Huang","submitted_at":"2024-01-19T15:39:49Z","abstract_excerpt":"While large language models (LLMs) have demonstrated exceptional performance across various tasks following human alignment, they may still generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. In this paper, we demonstrate the feasibility of mitigating hallucinations by verifying and minimizing the inconsistency between external knowledge present in the alignment data and the intrinsic knowledge embedded within foundation LLMs. Specifically, we propose a novel approach called Knowledge Consistent Alignment (KCA), which employs a well-al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.10768","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/2401.10768/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":"2401.10768","created_at":"2026-07-05T09:09:55.637603+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.10768v5","created_at":"2026-07-05T09:09:55.637603+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.10768","created_at":"2026-07-05T09:09:55.637603+00:00"},{"alias_kind":"pith_short_12","alias_value":"7TWQNAJ75CLK","created_at":"2026-07-05T09:09:55.637603+00:00"},{"alias_kind":"pith_short_16","alias_value":"7TWQNAJ75CLKPW6C","created_at":"2026-07-05T09:09:55.637603+00:00"},{"alias_kind":"pith_short_8","alias_value":"7TWQNAJ7","created_at":"2026-07-05T09:09:55.637603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.19689","citing_title":"Building Task Bots with Self-learning for Enhanced Adaptability, Extensibility, and Factuality","ref_index":179,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R","json":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R.json","graph_json":"https://pith.science/api/pith-number/7TWQNAJ75CLKPW6CPBUIGBAI7R/graph.json","events_json":"https://pith.science/api/pith-number/7TWQNAJ75CLKPW6CPBUIGBAI7R/events.json","paper":"https://pith.science/paper/7TWQNAJ7"},"agent_actions":{"view_html":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R","download_json":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R.json","view_paper":"https://pith.science/paper/7TWQNAJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.10768&json=true","fetch_graph":"https://pith.science/api/pith-number/7TWQNAJ75CLKPW6CPBUIGBAI7R/graph.json","fetch_events":"https://pith.science/api/pith-number/7TWQNAJ75CLKPW6CPBUIGBAI7R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R/action/storage_attestation","attest_author":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R/action/author_attestation","sign_citation":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R/action/citation_signature","submit_replication":"https://pith.science/pith/7TWQNAJ75CLKPW6CPBUIGBAI7R/action/replication_record"}},"created_at":"2026-07-05T09:09:55.637603+00:00","updated_at":"2026-07-05T09:09:55.637603+00:00"}