{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HUEJNTTLKMVRMPGCCJ4DM2752L","short_pith_number":"pith:HUEJNTTL","schema_version":"1.0","canonical_sha256":"3d0896ce6b532b163cc21278366bfdd2c367bbfb0d7b8c60231d1f6dbe2bf140","source":{"kind":"arxiv","id":"2305.13669","version":3},"attestation_state":"computed","paper":{"title":"The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Junzhou Zhao, Liangming Pan, Shuo Zhang, William Yang Wang","submitted_at":"2023-05-23T04:22:50Z","abstract_excerpt":"Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers. Yet even with the correct groundings in the reference, they can ignore them and rely on wrong groundings or their inherent biases to hallucinate when users, being largely unaware of the specifics of the stored information, pose questions that might not directly correlate with the retrieved groundings. In this work, we formulate this knowledge alignment problem and introduce MixAlign, a framework that interacts with both the human user and the knowledge base to obtain and integrate"},"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":"2305.13669","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-23T04:22:50Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"548aa41412388da1a4a69de57cc34ec10fe37a5e01e8be18bac3b03f69a99979","abstract_canon_sha256":"5bd9992173a56666b899a31277683a99915163020804e4579149252eea6aba1e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:31:03.772594Z","signature_b64":"a9QUHkRVc//WtsBU5m7Kv/7/LhMsZq50uYJ2Oyoyyp3tCrmwGpkTLKApFSiIdp1Gv9ViruuKoKlEFSdNZqm/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d0896ce6b532b163cc21278366bfdd2c367bbfb0d7b8c60231d1f6dbe2bf140","last_reissued_at":"2026-07-05T08:31:03.772080Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:31:03.772080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Knowledge Alignment Problem: Bridging Human and External Knowledge for Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Junzhou Zhao, Liangming Pan, Shuo Zhang, William Yang Wang","submitted_at":"2023-05-23T04:22:50Z","abstract_excerpt":"Large language models often necessitate grounding on external knowledge to generate faithful and reliable answers. Yet even with the correct groundings in the reference, they can ignore them and rely on wrong groundings or their inherent biases to hallucinate when users, being largely unaware of the specifics of the stored information, pose questions that might not directly correlate with the retrieved groundings. In this work, we formulate this knowledge alignment problem and introduce MixAlign, a framework that interacts with both the human user and the knowledge base to obtain and integrate"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.13669","kind":"arxiv","version":3},"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/2305.13669/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":"2305.13669","created_at":"2026-07-05T08:31:03.772137+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.13669v3","created_at":"2026-07-05T08:31:03.772137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.13669","created_at":"2026-07-05T08:31:03.772137+00:00"},{"alias_kind":"pith_short_12","alias_value":"HUEJNTTLKMVR","created_at":"2026-07-05T08:31:03.772137+00:00"},{"alias_kind":"pith_short_16","alias_value":"HUEJNTTLKMVRMPGC","created_at":"2026-07-05T08:31:03.772137+00:00"},{"alias_kind":"pith_short_8","alias_value":"HUEJNTTL","created_at":"2026-07-05T08:31:03.772137+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.08068","citing_title":"DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination","ref_index":139,"is_internal_anchor":false},{"citing_arxiv_id":"2406.15927","citing_title":"Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2309.05922","citing_title":"A Survey of Hallucination in Large Foundation Models","ref_index":149,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L","json":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L.json","graph_json":"https://pith.science/api/pith-number/HUEJNTTLKMVRMPGCCJ4DM2752L/graph.json","events_json":"https://pith.science/api/pith-number/HUEJNTTLKMVRMPGCCJ4DM2752L/events.json","paper":"https://pith.science/paper/HUEJNTTL"},"agent_actions":{"view_html":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L","download_json":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L.json","view_paper":"https://pith.science/paper/HUEJNTTL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.13669&json=true","fetch_graph":"https://pith.science/api/pith-number/HUEJNTTLKMVRMPGCCJ4DM2752L/graph.json","fetch_events":"https://pith.science/api/pith-number/HUEJNTTLKMVRMPGCCJ4DM2752L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L/action/storage_attestation","attest_author":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L/action/author_attestation","sign_citation":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L/action/citation_signature","submit_replication":"https://pith.science/pith/HUEJNTTLKMVRMPGCCJ4DM2752L/action/replication_record"}},"created_at":"2026-07-05T08:31:03.772137+00:00","updated_at":"2026-07-05T08:31:03.772137+00:00"}