{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:3MCPWGZLZSJORIFKCC2MTYYYRQ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fdf7878677a8aa86998a42a9dc7fb9c5a46b17336d8742be3c6056ef8f23fd26","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-28T03:09:42Z","title_canon_sha256":"2e133cedc4a38acaa1a22ad367751931a5e2654267c78c61257df1990b181502"},"schema_version":"1.0","source":{"id":"2403.19113","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.19113","created_at":"2026-07-05T08:01:44Z"},{"alias_kind":"arxiv_version","alias_value":"2403.19113v1","created_at":"2026-07-05T08:01:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.19113","created_at":"2026-07-05T08:01:44Z"},{"alias_kind":"pith_short_12","alias_value":"3MCPWGZLZSJO","created_at":"2026-07-05T08:01:44Z"},{"alias_kind":"pith_short_16","alias_value":"3MCPWGZLZSJORIFK","created_at":"2026-07-05T08:01:44Z"},{"alias_kind":"pith_short_8","alias_value":"3MCPWGZL","created_at":"2026-07-05T08:01:44Z"}],"graph_snapshots":[{"event_id":"sha256:6fb7a34a3e36f9c9d345bb3713bf6c121d17e95db8b6ef10bc09e2eeb2cd814c","target":"graph","created_at":"2026-07-05T08:01:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.19113/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The widespread adoption of Large Language Models (LLMs) has facilitated numerous benefits. However, hallucination is a significant concern. In response, Retrieval Augmented Generation (RAG) has emerged as a highly promising paradigm to improve LLM outputs by grounding them in factual information. RAG relies on textual entailment (TE) or similar methods to check if the text produced by LLMs is supported or contradicted, compared to retrieved documents. This paper argues that conventional TE methods are inadequate for spotting hallucinations in content generated by LLMs. For instance, consider a","authors_text":"Aman Chadha, Amitava Das, Amit P. Sheth, Krishnav Rajbangshi, Shravani Nag, S.M Towhidul Islam Tonmoy, Vipula Rawte","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-28T03:09:42Z","title":"FACTOID: FACtual enTailment fOr hallucInation Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.19113","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6aa04e80c76e07a507663b98ca78e3e11009947e3ca478913309853227828066","target":"record","created_at":"2026-07-05T08:01:44Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fdf7878677a8aa86998a42a9dc7fb9c5a46b17336d8742be3c6056ef8f23fd26","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-28T03:09:42Z","title_canon_sha256":"2e133cedc4a38acaa1a22ad367751931a5e2654267c78c61257df1990b181502"},"schema_version":"1.0","source":{"id":"2403.19113","kind":"arxiv","version":1}},"canonical_sha256":"db04fb1b2bcc92e8a0aa10b4c9e3188c0fea6f29f4a59bcc5b56ec78650912c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db04fb1b2bcc92e8a0aa10b4c9e3188c0fea6f29f4a59bcc5b56ec78650912c6","first_computed_at":"2026-07-05T08:01:44.678851Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:01:44.678851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/eZWONAvxoBl8VoH2ZPtkdfnvl0r6BsRBrlgMD+2zgrAjwjBFl3D8zVoJnni+D57HQciXPJwX5WD3Svo6DTLCw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:01:44.679460Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.19113","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6aa04e80c76e07a507663b98ca78e3e11009947e3ca478913309853227828066","sha256:6fb7a34a3e36f9c9d345bb3713bf6c121d17e95db8b6ef10bc09e2eeb2cd814c"],"state_sha256":"8f20af46ffbb8c2a6ef6b89b97520526b4f840360c2c4eba21de0d6a859ec100"}