{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W3ZR2MKKDSD366XAIT7UYJI7P3","short_pith_number":"pith:W3ZR2MKK","schema_version":"1.0","canonical_sha256":"b6f31d314a1c87bf7ae044ff4c251f7ec179141fa321759a50d531eafd094003","source":{"kind":"arxiv","id":"2408.17072","version":2},"attestation_state":"computed","paper":{"title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Binghui Guo, Hainan Zhang, Hongwei Zheng, Liang Pang, Yujing Wang, Zhiming Zheng","submitted_at":"2024-08-30T07:57:30Z","abstract_excerpt":"In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user's information needs. However, traditional context-based rewriting has minimal enhancement on downstream generation tasks due to the lengthy process from query rewriting to response generation. Some researchers try to utilize reinforcement learning with generation feedback to assist the rewriter, but these sparse rewards provide little guidance in most cases, leading to unstable training and generation results. We fin"},"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":"2408.17072","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-30T07:57:30Z","cross_cats_sorted":[],"title_canon_sha256":"bf5b20b49e4a2ef1bf3fcc22623f31d36d3b46fde091fd5c8b9822a792b4aa20","abstract_canon_sha256":"20f9d703a0b60a07ca89a19f86a279428087963f244e1c86f6a5dcfd8dbff366"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:36.090633Z","signature_b64":"PZS3j2zJNzQRSPiJKHLzgY0v6PORG6UfbpLwKU4+wcYfIvGTwBQHGw5r8RZQ4sgAenztXbAV0SJM+tsnO8A/Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6f31d314a1c87bf7ae044ff4c251f7ec179141fa321759a50d531eafd094003","last_reissued_at":"2026-07-05T09:51:36.090124Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:36.090124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MaFeRw: Query Rewriting with Multi-Aspect Feedbacks for Retrieval-Augmented Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Binghui Guo, Hainan Zhang, Hongwei Zheng, Liang Pang, Yujing Wang, Zhiming Zheng","submitted_at":"2024-08-30T07:57:30Z","abstract_excerpt":"In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user's information needs. However, traditional context-based rewriting has minimal enhancement on downstream generation tasks due to the lengthy process from query rewriting to response generation. Some researchers try to utilize reinforcement learning with generation feedback to assist the rewriter, but these sparse rewards provide little guidance in most cases, leading to unstable training and generation results. We fin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.17072","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/2408.17072/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":"2408.17072","created_at":"2026-07-05T09:51:36.090185+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.17072v2","created_at":"2026-07-05T09:51:36.090185+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.17072","created_at":"2026-07-05T09:51:36.090185+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3ZR2MKKDSD3","created_at":"2026-07-05T09:51:36.090185+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3ZR2MKKDSD366XA","created_at":"2026-07-05T09:51:36.090185+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3ZR2MKK","created_at":"2026-07-05T09:51:36.090185+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.00017","citing_title":"Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2607.00017","citing_title":"Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01248","citing_title":"$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09666","citing_title":"Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems","ref_index":37,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01248","citing_title":"$S^3$-R1: Learning to Retrieve and Answer Step-by-Step with Synthetic Data","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3","json":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3.json","graph_json":"https://pith.science/api/pith-number/W3ZR2MKKDSD366XAIT7UYJI7P3/graph.json","events_json":"https://pith.science/api/pith-number/W3ZR2MKKDSD366XAIT7UYJI7P3/events.json","paper":"https://pith.science/paper/W3ZR2MKK"},"agent_actions":{"view_html":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3","download_json":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3.json","view_paper":"https://pith.science/paper/W3ZR2MKK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.17072&json=true","fetch_graph":"https://pith.science/api/pith-number/W3ZR2MKKDSD366XAIT7UYJI7P3/graph.json","fetch_events":"https://pith.science/api/pith-number/W3ZR2MKKDSD366XAIT7UYJI7P3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3/action/storage_attestation","attest_author":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3/action/author_attestation","sign_citation":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3/action/citation_signature","submit_replication":"https://pith.science/pith/W3ZR2MKKDSD366XAIT7UYJI7P3/action/replication_record"}},"created_at":"2026-07-05T09:51:36.090185+00:00","updated_at":"2026-07-05T09:51:36.090185+00:00"}