{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:K7GUDGLNQRQCQIAUYOAKM6NHVV","short_pith_number":"pith:K7GUDGLN","schema_version":"1.0","canonical_sha256":"57cd41996d8460282014c380a679a7ad6351102768c9f0a6226da96f6ab390f8","source":{"kind":"arxiv","id":"2212.07769","version":2},"attestation_state":"computed","paper":{"title":"CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Lorenz Kuhn, Sebastian Farquhar, Yarin Gal","submitted_at":"2022-12-15T12:47:18Z","abstract_excerpt":"Users often ask dialogue systems ambiguous questions that require clarification. We show that current language models rarely ask users to clarify ambiguous questions and instead provide incorrect answers. To address this, we introduce CLAM: a framework for getting language models to selectively ask for clarification about ambiguous user questions. In particular, we show that we can prompt language models to detect whether a given question is ambiguous, generate an appropriate clarifying question to ask the user, and give a final answer after receiving clarification. We also show that we can si"},"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":"2212.07769","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-15T12:47:18Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"384acb66628802b770f73b303979484f923c334a721ef7e4505cb6070adc4f53","abstract_canon_sha256":"b8c6466c20f8be13b7f71e2bc7fbb36798b87a6aca76e486adb066beb8cc9f56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:18.223545Z","signature_b64":"ct5mgnTKJLCYoXbEGkC85M7nVd/Y12DGjVJrO7ftDPMnNDwt8UiVm/oAB4a0VtX3DQhFoDdW0ukpzGcVigInAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57cd41996d8460282014c380a679a7ad6351102768c9f0a6226da96f6ab390f8","last_reissued_at":"2026-07-05T05:43:18.223142Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:18.223142Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Lorenz Kuhn, Sebastian Farquhar, Yarin Gal","submitted_at":"2022-12-15T12:47:18Z","abstract_excerpt":"Users often ask dialogue systems ambiguous questions that require clarification. We show that current language models rarely ask users to clarify ambiguous questions and instead provide incorrect answers. To address this, we introduce CLAM: a framework for getting language models to selectively ask for clarification about ambiguous user questions. In particular, we show that we can prompt language models to detect whether a given question is ambiguous, generate an appropriate clarifying question to ask the user, and give a final answer after receiving clarification. We also show that we can si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.07769","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/2212.07769/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":"2212.07769","created_at":"2026-07-05T05:43:18.223198+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.07769v2","created_at":"2026-07-05T05:43:18.223198+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.07769","created_at":"2026-07-05T05:43:18.223198+00:00"},{"alias_kind":"pith_short_12","alias_value":"K7GUDGLNQRQC","created_at":"2026-07-05T05:43:18.223198+00:00"},{"alias_kind":"pith_short_16","alias_value":"K7GUDGLNQRQCQIAU","created_at":"2026-07-05T05:43:18.223198+00:00"},{"alias_kind":"pith_short_8","alias_value":"K7GUDGLN","created_at":"2026-07-05T05:43:18.223198+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.23590","citing_title":"The Topology of Ill-Posed Questions: Persistent Homology for Detection and Steering in LLMs","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2606.13220","citing_title":"LLM-as-an-Investigator: Evidence-First Reasoning for Robust Interactive Problem Diagnosis","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2606.12587","citing_title":"Strategic Decision Support for AI Agents","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2409.00557","citing_title":"Learning to Ask: When LLM Agents Meet Unclear Instruction","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2605.11240","citing_title":"When to Ask a Question: Understanding Communication Strategies in Generative AI Tools","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21827","citing_title":"Alignment has a Fantasia Problem","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19656","citing_title":"Pause or Fabricate? Training Language Models for Grounded Reasoning","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV","json":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV.json","graph_json":"https://pith.science/api/pith-number/K7GUDGLNQRQCQIAUYOAKM6NHVV/graph.json","events_json":"https://pith.science/api/pith-number/K7GUDGLNQRQCQIAUYOAKM6NHVV/events.json","paper":"https://pith.science/paper/K7GUDGLN"},"agent_actions":{"view_html":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV","download_json":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV.json","view_paper":"https://pith.science/paper/K7GUDGLN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.07769&json=true","fetch_graph":"https://pith.science/api/pith-number/K7GUDGLNQRQCQIAUYOAKM6NHVV/graph.json","fetch_events":"https://pith.science/api/pith-number/K7GUDGLNQRQCQIAUYOAKM6NHVV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV/action/storage_attestation","attest_author":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV/action/author_attestation","sign_citation":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV/action/citation_signature","submit_replication":"https://pith.science/pith/K7GUDGLNQRQCQIAUYOAKM6NHVV/action/replication_record"}},"created_at":"2026-07-05T05:43:18.223198+00:00","updated_at":"2026-07-05T05:43:18.223198+00:00"}