{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:PT4Y6HGAZMHNPNEWKHV7KD2SCP","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":"15e69d9cba5b57e7fccaf618e490af2ec4523001c530954e7fb5af1e364bf0c2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-24T18:48:43Z","title_canon_sha256":"e76cb4409e5a4f1b7c262877010a2f67881ae0ff3f7f7c5dbae51b8faca4f459"},"schema_version":"1.0","source":{"id":"2302.12813","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.12813","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"2302.12813v3","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.12813","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"PT4Y6HGAZMHN","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PT4Y6HGAZMHNPNEW","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PT4Y6HGA","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:1a89740b6954038cd7d55e5d749f7712f2133321042249a614ef9536d43f3a64","target":"graph","created_at":"2026-05-17T23:38:14Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That utility functions such as factuality scores can reliably detect and guide correction of hallucinations without introducing new errors or degrading response quality."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"LLM-Augmenter reduces hallucinations in LLMs like ChatGPT by grounding responses in external knowledge and using automated feedback loops to iteratively revise outputs."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"LLM-Augmenter augments black-box models like ChatGPT with external knowledge modules and automated feedback to reduce hallucinations while preserving fluency."}],"snapshot_sha256":"6008599ef363fb51d705b05be76bbedb830d302b6294e29ef95c0adbb64ecb56"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d242727f8af11c7ba870a1ddea1a8d1ede51b6906cbd44015231a29e4d082674"},"paper":{"abstract_excerpt":"Large language models (LLMs), such as ChatGPT, are able to generate human-like, fluent responses for many downstream tasks, e.g., task-oriented dialog and question answering. However, applying LLMs to real-world, mission-critical applications remains challenging mainly due to their tendency to generate hallucinations and their inability to use external knowledge. This paper proposes a LLM-Augmenter system, which augments a black-box LLM with a set of plug-and-play modules. Our system makes the LLM generate responses grounded in external knowledge, e.g., stored in task-specific databases. It al","authors_text":"Baolin Peng, Hao Cheng, Jianfeng Gao, Lars Liden, Michel Galley, Pengcheng He, Qiuyuan Huang, Weizhu Chen, Yu Hu, Yujia Xie, Zhou Yu","cross_cats":["cs.AI"],"headline":"LLM-Augmenter augments black-box models like ChatGPT with external knowledge modules and automated feedback to reduce hallucinations while preserving fluency.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-24T18:48:43Z","title":"Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.12813","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-17T09:56:55.544089Z","id":"57f018a2-bad6-4d6b-9075-36d0b856329c","model_set":{"reader":"grok-4.3"},"one_line_summary":"LLM-Augmenter reduces hallucinations in LLMs like ChatGPT by grounding responses in external knowledge and using automated feedback loops to iteratively revise outputs.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"LLM-Augmenter augments black-box models like ChatGPT with external knowledge modules and automated feedback to reduce hallucinations while preserving fluency.","strongest_claim":"LLM-Augmenter significantly reduces ChatGPT's hallucinations without sacrificing the fluency and informativeness of its responses.","weakest_assumption":"That utility functions such as factuality scores can reliably detect and guide correction of hallucinations without introducing new errors or degrading response quality."}},"verdict_id":"57f018a2-bad6-4d6b-9075-36d0b856329c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4eaa8e62cbfce8b06aea6c360cbc3f5a064364e4652203bd66a071f3117e8399","target":"record","created_at":"2026-05-17T23:38:14Z","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":"15e69d9cba5b57e7fccaf618e490af2ec4523001c530954e7fb5af1e364bf0c2","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-02-24T18:48:43Z","title_canon_sha256":"e76cb4409e5a4f1b7c262877010a2f67881ae0ff3f7f7c5dbae51b8faca4f459"},"schema_version":"1.0","source":{"id":"2302.12813","kind":"arxiv","version":3}},"canonical_sha256":"7cf98f1cc0cb0ed7b49651ebf50f5213c30335fbc700e348a272bdc58cae7d5e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7cf98f1cc0cb0ed7b49651ebf50f5213c30335fbc700e348a272bdc58cae7d5e","first_computed_at":"2026-05-17T23:38:14.429196Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:14.429196Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t5QfxXBwMHvRFXB2DzUxeidx0UCLN4ABq20cQb6ZBNq6tF71Lb/ezeCaLTgZMNzW/3QpmlkHxtDzDdf69EoDCQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:14.429920Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.12813","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4eaa8e62cbfce8b06aea6c360cbc3f5a064364e4652203bd66a071f3117e8399","sha256:1a89740b6954038cd7d55e5d749f7712f2133321042249a614ef9536d43f3a64"],"state_sha256":"aa859437c106c6c88e820ebf512a0c174128f190f4304f57d6c515e9fe0b860a"}