{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KTHYX3TQXOMZO3KUSOQDTRDZ6T","short_pith_number":"pith:KTHYX3TQ","schema_version":"1.0","canonical_sha256":"54cf8bee70bb99976d5493a039c479f4c57fe8552f2685ac031ab868c91341af","source":{"kind":"arxiv","id":"2312.12009","version":2},"attestation_state":"computed","paper":{"title":"Active Preference Inference using Language Models and Probabilistic Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kevin Ellis, Volodymyr Kuleshov, Wasu Top Piriyakulkij","submitted_at":"2023-12-19T09:58:54Z","abstract_excerpt":"Actively inferring user preferences, for example by asking good questions, is important for any human-facing decision-making system. Active inference allows such systems to adapt and personalize themselves to nuanced individual preferences. To enable this ability for instruction-tuned large language models (LLMs), one may prompt them to ask users questions to infer their preferences, transforming the language models into more robust, interactive systems. However, out of the box, these models are not efficient at extracting preferences: the questions they generate are not informative, requiring"},"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":"2312.12009","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-19T09:58:54Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4cac745d724db660cbfc5e983dd65a34f05eee3f3dec0325478242b7e6c3954f","abstract_canon_sha256":"2db0d0c6fc12b791f8d533f2f9daba7b816fff89b5a3f4f4dee354284985b6f8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:37:04.457427Z","signature_b64":"W2w+td/tMEAZB1HoO/9s7CPReGl2SYs+DzcK5UHDVQ9jg4eMoju1S8I4yzMFAnmhDJ637IFaRTU6lHsy5r4uCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54cf8bee70bb99976d5493a039c479f4c57fe8552f2685ac031ab868c91341af","last_reissued_at":"2026-07-05T08:37:04.457022Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:37:04.457022Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Active Preference Inference using Language Models and Probabilistic Reasoning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Kevin Ellis, Volodymyr Kuleshov, Wasu Top Piriyakulkij","submitted_at":"2023-12-19T09:58:54Z","abstract_excerpt":"Actively inferring user preferences, for example by asking good questions, is important for any human-facing decision-making system. Active inference allows such systems to adapt and personalize themselves to nuanced individual preferences. To enable this ability for instruction-tuned large language models (LLMs), one may prompt them to ask users questions to infer their preferences, transforming the language models into more robust, interactive systems. However, out of the box, these models are not efficient at extracting preferences: the questions they generate are not informative, requiring"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.12009","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/2312.12009/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":"2312.12009","created_at":"2026-07-05T08:37:04.457076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.12009v2","created_at":"2026-07-05T08:37:04.457076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.12009","created_at":"2026-07-05T08:37:04.457076+00:00"},{"alias_kind":"pith_short_12","alias_value":"KTHYX3TQXOMZ","created_at":"2026-07-05T08:37:04.457076+00:00"},{"alias_kind":"pith_short_16","alias_value":"KTHYX3TQXOMZO3KU","created_at":"2026-07-05T08:37:04.457076+00:00"},{"alias_kind":"pith_short_8","alias_value":"KTHYX3TQ","created_at":"2026-07-05T08:37:04.457076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02827","citing_title":"TO-GATE: Clarifying Questions and Summarizing Responses with Trajectory Optimization for Eliciting Human Preference","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T","json":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T.json","graph_json":"https://pith.science/api/pith-number/KTHYX3TQXOMZO3KUSOQDTRDZ6T/graph.json","events_json":"https://pith.science/api/pith-number/KTHYX3TQXOMZO3KUSOQDTRDZ6T/events.json","paper":"https://pith.science/paper/KTHYX3TQ"},"agent_actions":{"view_html":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T","download_json":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T.json","view_paper":"https://pith.science/paper/KTHYX3TQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.12009&json=true","fetch_graph":"https://pith.science/api/pith-number/KTHYX3TQXOMZO3KUSOQDTRDZ6T/graph.json","fetch_events":"https://pith.science/api/pith-number/KTHYX3TQXOMZO3KUSOQDTRDZ6T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T/action/storage_attestation","attest_author":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T/action/author_attestation","sign_citation":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T/action/citation_signature","submit_replication":"https://pith.science/pith/KTHYX3TQXOMZO3KUSOQDTRDZ6T/action/replication_record"}},"created_at":"2026-07-05T08:37:04.457076+00:00","updated_at":"2026-07-05T08:37:04.457076+00:00"}