{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UOIA2PEF335JBHSF3XURSNGJUS","short_pith_number":"pith:UOIA2PEF","schema_version":"1.0","canonical_sha256":"a3900d3c85defa909e45dde91934c9a4830173fbd11585ce088b1ff62cfadf0c","source":{"kind":"arxiv","id":"2404.10981","version":2},"attestation_state":"computed","paper":{"title":"A Survey on Retrieval-Augmented Text Generation for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Jimmy Huang, Yizheng Huang","submitted_at":"2024-04-17T01:27:42Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this "},"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":"2404.10981","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2024-04-17T01:27:42Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"8fc661a74d8718c026dc8496486ba1ddb7f63949dba4d830e6538e3a0c4eca40","abstract_canon_sha256":"fd60332b6cfee3dd95832b448727df6cf71b14a8edf74735b633c6ba51c95225"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:04.284949Z","signature_b64":"W7u0u0FdOLTy2QK9xm5yReBxmE1ZMIbPSRsOgktEZe6rWAk1lsVNgsL6/1Mz8UDU8NphdC+9DbnXqbGTgbZjBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3900d3c85defa909e45dde91934c9a4830173fbd11585ce088b1ff62cfadf0c","last_reissued_at":"2026-05-20T00:04:04.284271Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:04.284271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey on Retrieval-Augmented Text Generation for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.IR","authors_text":"Jimmy Huang, Yizheng Huang","submitted_at":"2024-04-17T01:27:42Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) merges retrieval methods with deep learning advancements to address the static limitations of large language models (LLMs) by enabling the dynamic integration of up-to-date external information. This methodology, focusing primarily on the text domain, provides a cost-effective solution to the generation of plausible but possibly incorrect responses by LLMs, thereby enhancing the accuracy and reliability of their outputs through the use of real-world data. As RAG grows in complexity and incorporates multiple concepts that can influence its performance, this "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10981","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/2404.10981/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":"2404.10981","created_at":"2026-05-20T00:04:04.284372+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.10981v2","created_at":"2026-05-20T00:04:04.284372+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10981","created_at":"2026-05-20T00:04:04.284372+00:00"},{"alias_kind":"pith_short_12","alias_value":"UOIA2PEF335J","created_at":"2026-05-20T00:04:04.284372+00:00"},{"alias_kind":"pith_short_16","alias_value":"UOIA2PEF335JBHSF","created_at":"2026-05-20T00:04:04.284372+00:00"},{"alias_kind":"pith_short_8","alias_value":"UOIA2PEF","created_at":"2026-05-20T00:04:04.284372+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":8,"sample":[{"citing_arxiv_id":"2605.22829","citing_title":"LFRAG: Layout-oriented Fine-grained Retrieval-Augmented Generation on Multimodal Document Understanding","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2502.09891","citing_title":"ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2503.04338","citing_title":"In-depth Analysis of Graph-based RAG in a Unified Framework","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18769","citing_title":"ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation","ref_index":70,"is_internal_anchor":true},{"citing_arxiv_id":"2601.12538","citing_title":"Agentic Reasoning for Large Language Models","ref_index":10,"is_internal_anchor":true},{"citing_arxiv_id":"2511.20857","citing_title":"Evo-Memory: Benchmarking LLM Agent Test-time Learning with Self-Evolving Memory","ref_index":189,"is_internal_anchor":true},{"citing_arxiv_id":"2604.06666","citing_title":"A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM","ref_index":31,"is_internal_anchor":true},{"citing_arxiv_id":"2604.17458","citing_title":"EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval","ref_index":289,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS","json":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS.json","graph_json":"https://pith.science/api/pith-number/UOIA2PEF335JBHSF3XURSNGJUS/graph.json","events_json":"https://pith.science/api/pith-number/UOIA2PEF335JBHSF3XURSNGJUS/events.json","paper":"https://pith.science/paper/UOIA2PEF"},"agent_actions":{"view_html":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS","download_json":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS.json","view_paper":"https://pith.science/paper/UOIA2PEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.10981&json=true","fetch_graph":"https://pith.science/api/pith-number/UOIA2PEF335JBHSF3XURSNGJUS/graph.json","fetch_events":"https://pith.science/api/pith-number/UOIA2PEF335JBHSF3XURSNGJUS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS/action/storage_attestation","attest_author":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS/action/author_attestation","sign_citation":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS/action/citation_signature","submit_replication":"https://pith.science/pith/UOIA2PEF335JBHSF3XURSNGJUS/action/replication_record"}},"created_at":"2026-05-20T00:04:04.284372+00:00","updated_at":"2026-05-20T00:04:04.284372+00:00"}