{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IREJX6KBNI24U4ZHTJZ5JXWFA6","short_pith_number":"pith:IREJX6KB","schema_version":"1.0","canonical_sha256":"44489bf9416a35ca73279a73d4dec5079b7f6b4aba63ef335bc74d034b4351ed","source":{"kind":"arxiv","id":"2306.05212","version":1},"attestation_state":"computed","paper":{"title":"RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jiajie Jin, Jiehan Cheng, Jiongnan Liu, Ji-Rong Wen, Zhicheng Dou, Zihan Wang","submitted_at":"2023-06-08T14:10:54Z","abstract_excerpt":"Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to user requests. This problem can be alleviated by augmenting LLMs with information retrieval (IR) systems (also known as retrieval-augmented LLMs). Applying this strategy, LLMs can generate more factual texts in response to user input according to the relevant content retrieved by IR systems from external corpora as references. In addition, by incorporating external knowledge, retrieval-augmented LLMs can answer in-dom"},"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":"2306.05212","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.IR","submitted_at":"2023-06-08T14:10:54Z","cross_cats_sorted":[],"title_canon_sha256":"87de233b4c15bcf7a1b3d317ce7005e8004d97e452f369acff2c715862af1f1a","abstract_canon_sha256":"969657f065c706a58c0e25da253dcc8d6f2342d209019806c0bef9ae377fe2c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:18:53.738536Z","signature_b64":"NJQ0BBFDZVH94Cv/QY6ZTN/rZU/rVtr9iMPAAdA1cQi4DODaUMBeHs2piSiHDi4QKiygAWeF2FxkwdL1BKhQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44489bf9416a35ca73279a73d4dec5079b7f6b4aba63ef335bc74d034b4351ed","last_reissued_at":"2026-07-05T06:18:53.738093Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:18:53.738093Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Jiajie Jin, Jiehan Cheng, Jiongnan Liu, Ji-Rong Wen, Zhicheng Dou, Zihan Wang","submitted_at":"2023-06-08T14:10:54Z","abstract_excerpt":"Although Large Language Models (LLMs) have demonstrated extraordinary capabilities in many domains, they still have a tendency to hallucinate and generate fictitious responses to user requests. This problem can be alleviated by augmenting LLMs with information retrieval (IR) systems (also known as retrieval-augmented LLMs). Applying this strategy, LLMs can generate more factual texts in response to user input according to the relevant content retrieved by IR systems from external corpora as references. In addition, by incorporating external knowledge, retrieval-augmented LLMs can answer in-dom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.05212","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/2306.05212/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":"2306.05212","created_at":"2026-07-05T06:18:53.738154+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.05212v1","created_at":"2026-07-05T06:18:53.738154+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.05212","created_at":"2026-07-05T06:18:53.738154+00:00"},{"alias_kind":"pith_short_12","alias_value":"IREJX6KBNI24","created_at":"2026-07-05T06:18:53.738154+00:00"},{"alias_kind":"pith_short_16","alias_value":"IREJX6KBNI24U4ZH","created_at":"2026-07-05T06:18:53.738154+00:00"},{"alias_kind":"pith_short_8","alias_value":"IREJX6KB","created_at":"2026-07-05T06:18:53.738154+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.07912","citing_title":"Elevating Legal LLM Responses: Harnessing Trainable Logical Structures and Semantic Knowledge with Legal Reasoning","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6","json":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6.json","graph_json":"https://pith.science/api/pith-number/IREJX6KBNI24U4ZHTJZ5JXWFA6/graph.json","events_json":"https://pith.science/api/pith-number/IREJX6KBNI24U4ZHTJZ5JXWFA6/events.json","paper":"https://pith.science/paper/IREJX6KB"},"agent_actions":{"view_html":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6","download_json":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6.json","view_paper":"https://pith.science/paper/IREJX6KB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.05212&json=true","fetch_graph":"https://pith.science/api/pith-number/IREJX6KBNI24U4ZHTJZ5JXWFA6/graph.json","fetch_events":"https://pith.science/api/pith-number/IREJX6KBNI24U4ZHTJZ5JXWFA6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6/action/storage_attestation","attest_author":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6/action/author_attestation","sign_citation":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6/action/citation_signature","submit_replication":"https://pith.science/pith/IREJX6KBNI24U4ZHTJZ5JXWFA6/action/replication_record"}},"created_at":"2026-07-05T06:18:53.738154+00:00","updated_at":"2026-07-05T06:18:53.738154+00:00"}