{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:D4XXVKUCNWQIDDNXGRBM525CHT","short_pith_number":"pith:D4XXVKUC","schema_version":"1.0","canonical_sha256":"1f2f7aaa826da0818db73442ceeba23cf1d0c086b49982c3014e813432a35fc5","source":{"kind":"arxiv","id":"2310.03951","version":2},"attestation_state":"computed","paper":{"title":"Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Deren Lei, Emily Ching, Eslam Kamal, Mengya Hu, Mingyu Wang, Vincent Yun, Yaxi Li","submitted_at":"2023-10-06T00:10:46Z","abstract_excerpt":"Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art p"},"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":"2310.03951","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T00:10:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9e42b95c95ac4636fcefb151eaba437f1f61a12d6b614702bc07eb27fc4e229e","abstract_canon_sha256":"f98083b7be755da9df225b3f8c95bce62182409c2449649b48b5719cca48798a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:59:00.535199Z","signature_b64":"Q1wicslxnsztRQVO3G+cVc4u2qTJkto1qUMam6RuE9XWt895i8GovQIW3t4L17htLyv9mMUqgBuR4rlR57MJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f2f7aaa826da0818db73442ceeba23cf1d0c086b49982c3014e813432a35fc5","last_reissued_at":"2026-07-05T06:59:00.534761Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:59:00.534761Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Deren Lei, Emily Ching, Eslam Kamal, Mengya Hu, Mingyu Wang, Vincent Yun, Yaxi Li","submitted_at":"2023-10-06T00:10:46Z","abstract_excerpt":"Large language models (LLMs) can generate fluent natural language texts when given relevant documents as background context. This ability has attracted considerable interest in developing industry applications of LLMs. However, LLMs are prone to generate hallucinations that are not supported by the provided sources. In this paper, we propose a hierarchical framework to detect and mitigate such ungrounded hallucination. Our framework uses Chain of Natural Language Inference (CoNLI) for hallucination detection and hallucination reduction via post-editing. Our approach achieves state-of-the-art p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03951","kind":"arxiv","version":2},"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/2310.03951/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":"2310.03951","created_at":"2026-07-05T06:59:00.534817+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.03951v2","created_at":"2026-07-05T06:59:00.534817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03951","created_at":"2026-07-05T06:59:00.534817+00:00"},{"alias_kind":"pith_short_12","alias_value":"D4XXVKUCNWQI","created_at":"2026-07-05T06:59:00.534817+00:00"},{"alias_kind":"pith_short_16","alias_value":"D4XXVKUCNWQIDDNX","created_at":"2026-07-05T06:59:00.534817+00:00"},{"alias_kind":"pith_short_8","alias_value":"D4XXVKUC","created_at":"2026-07-05T06:59:00.534817+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.27679","citing_title":"From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models","ref_index":55,"is_internal_anchor":false},{"citing_arxiv_id":"2311.05232","citing_title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","ref_index":167,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT","json":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT.json","graph_json":"https://pith.science/api/pith-number/D4XXVKUCNWQIDDNXGRBM525CHT/graph.json","events_json":"https://pith.science/api/pith-number/D4XXVKUCNWQIDDNXGRBM525CHT/events.json","paper":"https://pith.science/paper/D4XXVKUC"},"agent_actions":{"view_html":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT","download_json":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT.json","view_paper":"https://pith.science/paper/D4XXVKUC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.03951&json=true","fetch_graph":"https://pith.science/api/pith-number/D4XXVKUCNWQIDDNXGRBM525CHT/graph.json","fetch_events":"https://pith.science/api/pith-number/D4XXVKUCNWQIDDNXGRBM525CHT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT/action/storage_attestation","attest_author":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT/action/author_attestation","sign_citation":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT/action/citation_signature","submit_replication":"https://pith.science/pith/D4XXVKUCNWQIDDNXGRBM525CHT/action/replication_record"}},"created_at":"2026-07-05T06:59:00.534817+00:00","updated_at":"2026-07-05T06:59:00.534817+00:00"}