{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JPWZX4UNCG3T6ASZQJV63DOFHR","short_pith_number":"pith:JPWZX4UN","schema_version":"1.0","canonical_sha256":"4bed9bf28d11b73f0259826bed8dc53c4c689276e216f299530417e27ac2e4f5","source":{"kind":"arxiv","id":"2404.03491","version":1},"attestation_state":"computed","paper":{"title":"A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jifan Yu, Jing Zhang, Juanzi Li, Lei Hou, Xiaohan Zhang, Xuanyu Lei, Yifan Xu, Zijun Yao","submitted_at":"2024-04-04T14:45:26Z","abstract_excerpt":"Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the hallucination problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good"},"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":"2404.03491","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-04-04T14:45:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7e28128dcafa45527fc4d6aa0ef888992e0519cb333d831c637fc8466b67de26","abstract_canon_sha256":"a649b472e783582f9277b9b5e7970376f015c5f94b55f7d989fae7dc06290e6d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:04:26.631266Z","signature_b64":"hfxbXq4YmuenJUPyRXN/adqzwHgNIUsiRKawjksRmKNT7WeELPIzfMI1pDU8/KkjtrOUwAjxr7so614ER0B5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4bed9bf28d11b73f0259826bed8dc53c4c689276e216f299530417e27ac2e4f5","last_reissued_at":"2026-07-05T08:04:26.630767Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:04:26.630767Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Cause-Effect Look at Alleviating Hallucination of Knowledge-grounded Dialogue Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jifan Yu, Jing Zhang, Juanzi Li, Lei Hou, Xiaohan Zhang, Xuanyu Lei, Yifan Xu, Zijun Yao","submitted_at":"2024-04-04T14:45:26Z","abstract_excerpt":"Empowered by the large-scale pretrained language models, existing dialogue systems have demonstrated impressive performance conducting fluent and natural-sounding conversations. However, they are still plagued by the hallucination problem, causing unpredictable factual errors in the generated responses. Recently, knowledge-grounded dialogue generation models, that intentionally invoke external knowledge resources to more informative responses, are also proven to be effective in reducing hallucination. Following the idea of getting high-quality knowledge, a few efforts have achieved pretty good"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03491","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/2404.03491/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":"2404.03491","created_at":"2026-07-05T08:04:26.630828+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.03491v1","created_at":"2026-07-05T08:04:26.630828+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.03491","created_at":"2026-07-05T08:04:26.630828+00:00"},{"alias_kind":"pith_short_12","alias_value":"JPWZX4UNCG3T","created_at":"2026-07-05T08:04:26.630828+00:00"},{"alias_kind":"pith_short_16","alias_value":"JPWZX4UNCG3T6ASZ","created_at":"2026-07-05T08:04:26.630828+00:00"},{"alias_kind":"pith_short_8","alias_value":"JPWZX4UN","created_at":"2026-07-05T08:04:26.630828+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/JPWZX4UNCG3T6ASZQJV63DOFHR","json":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR.json","graph_json":"https://pith.science/api/pith-number/JPWZX4UNCG3T6ASZQJV63DOFHR/graph.json","events_json":"https://pith.science/api/pith-number/JPWZX4UNCG3T6ASZQJV63DOFHR/events.json","paper":"https://pith.science/paper/JPWZX4UN"},"agent_actions":{"view_html":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR","download_json":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR.json","view_paper":"https://pith.science/paper/JPWZX4UN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.03491&json=true","fetch_graph":"https://pith.science/api/pith-number/JPWZX4UNCG3T6ASZQJV63DOFHR/graph.json","fetch_events":"https://pith.science/api/pith-number/JPWZX4UNCG3T6ASZQJV63DOFHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR/action/storage_attestation","attest_author":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR/action/author_attestation","sign_citation":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR/action/citation_signature","submit_replication":"https://pith.science/pith/JPWZX4UNCG3T6ASZQJV63DOFHR/action/replication_record"}},"created_at":"2026-07-05T08:04:26.630828+00:00","updated_at":"2026-07-05T08:04:26.630828+00:00"}