{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QJ4S6FS6UV6AZFYVKZ44VLMAXB","short_pith_number":"pith:QJ4S6FS6","schema_version":"1.0","canonical_sha256":"82792f165ea57c0c97155679caad80b84c9040ecd78233b0ddc130a131d31727","source":{"kind":"arxiv","id":"2503.11924","version":2},"attestation_state":"computed","paper":{"title":"REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ambarish Jash, Anushya Subbiah, Hubert Pham, James Pine, Krishna Sayana, Kun Su, Liam Hebert, Marialena Kyriakidi, Raghavendra Vasudeva, Sukhdeep Sodhi, Yuri Vasilevski","submitted_at":"2025-03-14T23:47:46Z","abstract_excerpt":"This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key natural language features: (1) user critiques, representing user \"steering\" queries that lead to the selection of a subsequent item, and (2) narratives, rich textual outputs associated with each recommended item taking into account prior c"},"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":"2503.11924","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-14T23:47:46Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"c0d40c716d4d037b5d692801fb3ae7f2c8d8d1088a584970c0d55970cb445249","abstract_canon_sha256":"3140d252b41bcb468ea558e9edb41edc6cba6dd9f0882d3963e6321248cc7d87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:35:33.915611Z","signature_b64":"jItKUKLTUHUjP3nIZ/IiC6O3d+mBMWUi1n/wudURYd5Niy+Z2+SI7qv5JcaieW10EbNxzQ9gfQeXDytY2sw7CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"82792f165ea57c0c97155679caad80b84c9040ecd78233b0ddc130a131d31727","last_reissued_at":"2026-07-05T11:35:33.915151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:35:33.915151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"REGEN: A Dataset and Benchmarks with Natural Language Critiques and Narratives","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Ambarish Jash, Anushya Subbiah, Hubert Pham, James Pine, Krishna Sayana, Kun Su, Liam Hebert, Marialena Kyriakidi, Raghavendra Vasudeva, Sukhdeep Sodhi, Yuri Vasilevski","submitted_at":"2025-03-14T23:47:46Z","abstract_excerpt":"This paper introduces a novel dataset REGEN (Reviews Enhanced with GEnerative Narratives), designed to benchmark the conversational capabilities of recommender Large Language Models (LLMs), addressing the limitations of existing datasets that primarily focus on sequential item prediction. REGEN extends the Amazon Product Reviews dataset by inpainting two key natural language features: (1) user critiques, representing user \"steering\" queries that lead to the selection of a subsequent item, and (2) narratives, rich textual outputs associated with each recommended item taking into account prior c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11924","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/2503.11924/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":"2503.11924","created_at":"2026-07-05T11:35:33.915207+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.11924v2","created_at":"2026-07-05T11:35:33.915207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11924","created_at":"2026-07-05T11:35:33.915207+00:00"},{"alias_kind":"pith_short_12","alias_value":"QJ4S6FS6UV6A","created_at":"2026-07-05T11:35:33.915207+00:00"},{"alias_kind":"pith_short_16","alias_value":"QJ4S6FS6UV6AZFYV","created_at":"2026-07-05T11:35:33.915207+00:00"},{"alias_kind":"pith_short_8","alias_value":"QJ4S6FS6","created_at":"2026-07-05T11:35:33.915207+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB","json":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB.json","graph_json":"https://pith.science/api/pith-number/QJ4S6FS6UV6AZFYVKZ44VLMAXB/graph.json","events_json":"https://pith.science/api/pith-number/QJ4S6FS6UV6AZFYVKZ44VLMAXB/events.json","paper":"https://pith.science/paper/QJ4S6FS6"},"agent_actions":{"view_html":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB","download_json":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB.json","view_paper":"https://pith.science/paper/QJ4S6FS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.11924&json=true","fetch_graph":"https://pith.science/api/pith-number/QJ4S6FS6UV6AZFYVKZ44VLMAXB/graph.json","fetch_events":"https://pith.science/api/pith-number/QJ4S6FS6UV6AZFYVKZ44VLMAXB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB/action/storage_attestation","attest_author":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB/action/author_attestation","sign_citation":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB/action/citation_signature","submit_replication":"https://pith.science/pith/QJ4S6FS6UV6AZFYVKZ44VLMAXB/action/replication_record"}},"created_at":"2026-07-05T11:35:33.915207+00:00","updated_at":"2026-07-05T11:35:33.915207+00:00"}