{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:ZVJXHMCGQIVQCF7UOT3QYJCCZN","short_pith_number":"pith:ZVJXHMCG","schema_version":"1.0","canonical_sha256":"cd5373b046822b0117f474f70c2442cb6b135fac9b8bea7829f013b31beaa008","source":{"kind":"arxiv","id":"2210.15718","version":1},"attestation_state":"computed","paper":{"title":"QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Anupam Samanta, Karthik Raman, Krishna Srinivasan, Lingrui Liao, Luca Bertelli, Mike Bendersky","submitted_at":"2022-10-27T18:44:58Z","abstract_excerpt":"Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lack of nuance or context. Complicated feature engineering efforts do not always lead to downstream improvements as their performance benefits may be offset by increased complexity of knowledge distillation. Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding. While Retrieval Augm"},"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":"2210.15718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-27T18:44:58Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"2abe1d70b86791f12abd4ee08d1fec4f52b13f641bc098a53c05af0e2a5a9cf6","abstract_canon_sha256":"6e4d343c4d1a41860069f05c65a6d2514be264a6ca5880f5dc35827c3d0bcc7e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:10.170182Z","signature_b64":"h1P+m/NowCf1zfZVLyJHpCsjCkFA4Irbaon2JWpYZyWS6y5wHVw+e7Qb8Q1tWh3PoQIkbdN4Lp3nEKTXnAANAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd5373b046822b0117f474f70c2442cb6b135fac9b8bea7829f013b31beaa008","last_reissued_at":"2026-07-05T05:11:10.169757Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:10.169757Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Anupam Samanta, Karthik Raman, Krishna Srinivasan, Lingrui Liao, Luca Bertelli, Mike Bendersky","submitted_at":"2022-10-27T18:44:58Z","abstract_excerpt":"Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks. Search queries though pose a unique challenge, given their short-length and lack of nuance or context. Complicated feature engineering efforts do not always lead to downstream improvements as their performance benefits may be offset by increased complexity of knowledge distillation. Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding. While Retrieval Augm"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.15718","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/2210.15718/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":"2210.15718","created_at":"2026-07-05T05:11:10.169817+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.15718v1","created_at":"2026-07-05T05:11:10.169817+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.15718","created_at":"2026-07-05T05:11:10.169817+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVJXHMCGQIVQ","created_at":"2026-07-05T05:11:10.169817+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVJXHMCGQIVQCF7U","created_at":"2026-07-05T05:11:10.169817+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVJXHMCG","created_at":"2026-07-05T05:11:10.169817+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10021","citing_title":"Enhancing Healthcare Search Intent Recognition with Query Representation Learning and Session Context","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN","json":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN.json","graph_json":"https://pith.science/api/pith-number/ZVJXHMCGQIVQCF7UOT3QYJCCZN/graph.json","events_json":"https://pith.science/api/pith-number/ZVJXHMCGQIVQCF7UOT3QYJCCZN/events.json","paper":"https://pith.science/paper/ZVJXHMCG"},"agent_actions":{"view_html":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN","download_json":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN.json","view_paper":"https://pith.science/paper/ZVJXHMCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.15718&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVJXHMCGQIVQCF7UOT3QYJCCZN/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVJXHMCGQIVQCF7UOT3QYJCCZN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN/action/storage_attestation","attest_author":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN/action/author_attestation","sign_citation":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN/action/citation_signature","submit_replication":"https://pith.science/pith/ZVJXHMCGQIVQCF7UOT3QYJCCZN/action/replication_record"}},"created_at":"2026-07-05T05:11:10.169817+00:00","updated_at":"2026-07-05T05:11:10.169817+00:00"}