{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:JCORMHNJVN3TW24ONXDTEWUJK6","short_pith_number":"pith:JCORMHNJ","schema_version":"1.0","canonical_sha256":"489d161da9ab773b6b8e6dc7325a89578b082eb4b6bd9a848a1a7785f0e691c6","source":{"kind":"arxiv","id":"2301.00303","version":1},"attestation_state":"computed","paper":{"title":"Rethinking with Retrieval: Faithful Large Language Model Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Roth, Hangfeng He, Hongming Zhang","submitted_at":"2022-12-31T22:35:34Z","abstract_excerpt":"Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the dec"},"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":"2301.00303","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-31T22:35:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5a8b14187f90a29e05870ce709e67860482b22b438658e15a54125295ded9d9d","abstract_canon_sha256":"c11205e946e319f253e8a96cf4f72536e461582145369947537d92ee69ca2072"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:29:44.984758Z","signature_b64":"cq9TTztqjN190X0LziKtvICznaQgjE2xewV4hk++Tr7mmy68PVJegVzYBqL24RQENrwGVKGtIcU/OMr7vEkyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"489d161da9ab773b6b8e6dc7325a89578b082eb4b6bd9a848a1a7785f0e691c6","last_reissued_at":"2026-07-05T05:29:44.984236Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:29:44.984236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking with Retrieval: Faithful Large Language Model Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Roth, Hangfeng He, Hongming Zhang","submitted_at":"2022-12-31T22:35:34Z","abstract_excerpt":"Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the dec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.00303","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/2301.00303/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":"2301.00303","created_at":"2026-07-05T05:29:44.984303+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.00303v1","created_at":"2026-07-05T05:29:44.984303+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.00303","created_at":"2026-07-05T05:29:44.984303+00:00"},{"alias_kind":"pith_short_12","alias_value":"JCORMHNJVN3T","created_at":"2026-07-05T05:29:44.984303+00:00"},{"alias_kind":"pith_short_16","alias_value":"JCORMHNJVN3TW24O","created_at":"2026-07-05T05:29:44.984303+00:00"},{"alias_kind":"pith_short_8","alias_value":"JCORMHNJ","created_at":"2026-07-05T05:29:44.984303+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.13171","citing_title":"NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2506.17585","citing_title":"Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16422","citing_title":"Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG","ref_index":13,"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":117,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00195","citing_title":"Diversity in Large Language Models under Supervised Fine-Tuning","ref_index":82,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13660","citing_title":"VRAG-DFD: Verifiable Retrieval-Augmentation for MLLM-based Deepfake Detection","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22849","citing_title":"R$^3$AG: Retriever Routing for Retrieval-Augmented Generation","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2605.00195","citing_title":"Diversity in Large Language Models under Supervised Fine-Tuning","ref_index":82,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6","json":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6.json","graph_json":"https://pith.science/api/pith-number/JCORMHNJVN3TW24ONXDTEWUJK6/graph.json","events_json":"https://pith.science/api/pith-number/JCORMHNJVN3TW24ONXDTEWUJK6/events.json","paper":"https://pith.science/paper/JCORMHNJ"},"agent_actions":{"view_html":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6","download_json":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6.json","view_paper":"https://pith.science/paper/JCORMHNJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.00303&json=true","fetch_graph":"https://pith.science/api/pith-number/JCORMHNJVN3TW24ONXDTEWUJK6/graph.json","fetch_events":"https://pith.science/api/pith-number/JCORMHNJVN3TW24ONXDTEWUJK6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6/action/storage_attestation","attest_author":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6/action/author_attestation","sign_citation":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6/action/citation_signature","submit_replication":"https://pith.science/pith/JCORMHNJVN3TW24ONXDTEWUJK6/action/replication_record"}},"created_at":"2026-07-05T05:29:44.984303+00:00","updated_at":"2026-07-05T05:29:44.984303+00:00"}