{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:Z544TFW2GQ2BTX37RWFNVC3TUV","short_pith_number":"pith:Z544TFW2","schema_version":"1.0","canonical_sha256":"cf79c996da343419df7f8d8ada8b73a54ae81660e26b35f3e95a99a8e851f748","source":{"kind":"arxiv","id":"2406.18064","version":3},"attestation_state":"computed","paper":{"title":"Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alberto Garcia Hernandez, Nicholas Kersting, Roman Kyslyi, Yang Wang","submitted_at":"2024-06-26T04:49:41Z","abstract_excerpt":"We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval, a novel grading system that is designed to assess correctness, completeness, and honesty. We further map the grading of quality aspects aforementioned into a binary score, indicating an accept or reject decision, mirroring the intuitive \"thumbs-up\" or \"thumbs-down\" gesture commonly used in chat applications. This approach suits factual business contexts where a clear decision opinion is essential. Our assessment applies vRAG-Eval to two Large Language Models (LLMs"},"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":"2406.18064","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-26T04:49:41Z","cross_cats_sorted":[],"title_canon_sha256":"ad174dcce7de6ad464e3e444bd22c2e25635d0beb5435a8ab3e4cf4683099ec6","abstract_canon_sha256":"19894ff12a6dc1edf98ee0f3b75761e769e878b8e67ab701b25baade94ae4e43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:10.335555Z","signature_b64":"umvF3SYtOTO2wwTWn9s8DsXqRG2nAEJMZrKA4gXGwC9wg2Ri+PzRqrlbiz94XpaBp2esXOzqarRKm9v4vUWzCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf79c996da343419df7f8d8ada8b73a54ae81660e26b35f3e95a99a8e851f748","last_reissued_at":"2026-07-05T09:32:10.335040Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:10.335040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Quality of Answers for Retrieval-Augmented Generation: A Strong LLM Is All You Need","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alberto Garcia Hernandez, Nicholas Kersting, Roman Kyslyi, Yang Wang","submitted_at":"2024-06-26T04:49:41Z","abstract_excerpt":"We present a comprehensive study of answer quality evaluation in Retrieval-Augmented Generation (RAG) applications using vRAG-Eval, a novel grading system that is designed to assess correctness, completeness, and honesty. We further map the grading of quality aspects aforementioned into a binary score, indicating an accept or reject decision, mirroring the intuitive \"thumbs-up\" or \"thumbs-down\" gesture commonly used in chat applications. This approach suits factual business contexts where a clear decision opinion is essential. Our assessment applies vRAG-Eval to two Large Language Models (LLMs"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18064","kind":"arxiv","version":3},"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/2406.18064/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":"2406.18064","created_at":"2026-07-05T09:32:10.335104+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.18064v3","created_at":"2026-07-05T09:32:10.335104+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18064","created_at":"2026-07-05T09:32:10.335104+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z544TFW2GQ2B","created_at":"2026-07-05T09:32:10.335104+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z544TFW2GQ2BTX37","created_at":"2026-07-05T09:32:10.335104+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z544TFW2","created_at":"2026-07-05T09:32:10.335104+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2506.20821","citing_title":"MultiFinRAG: An Optimized Multimodal Retrieval-Augmented Generation (RAG) Framework for Financial Question Answering","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV","json":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV.json","graph_json":"https://pith.science/api/pith-number/Z544TFW2GQ2BTX37RWFNVC3TUV/graph.json","events_json":"https://pith.science/api/pith-number/Z544TFW2GQ2BTX37RWFNVC3TUV/events.json","paper":"https://pith.science/paper/Z544TFW2"},"agent_actions":{"view_html":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV","download_json":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV.json","view_paper":"https://pith.science/paper/Z544TFW2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.18064&json=true","fetch_graph":"https://pith.science/api/pith-number/Z544TFW2GQ2BTX37RWFNVC3TUV/graph.json","fetch_events":"https://pith.science/api/pith-number/Z544TFW2GQ2BTX37RWFNVC3TUV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV/action/storage_attestation","attest_author":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV/action/author_attestation","sign_citation":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV/action/citation_signature","submit_replication":"https://pith.science/pith/Z544TFW2GQ2BTX37RWFNVC3TUV/action/replication_record"}},"created_at":"2026-07-05T09:32:10.335104+00:00","updated_at":"2026-07-05T09:32:10.335104+00:00"}