{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CE7S7XWY762TECSOP7XPALHY2B","short_pith_number":"pith:CE7S7XWY","schema_version":"1.0","canonical_sha256":"113f2fded8ffb5320a4e7feef02cf8d053b38af811481fb6d2aac6bea31485ba","source":{"kind":"arxiv","id":"2506.02449","version":1},"attestation_state":"computed","paper":{"title":"IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Bo Peng, Chaochao Lu, Heyang Gong, Zhiheng Wang","submitted_at":"2025-06-03T05:14:11Z","abstract_excerpt":"In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the Implicit Personalized Dialogue (IP-Dialog) benchmark along with a training dataset, covering "},"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":"2506.02449","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-03T05:14:11Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"0d6a1398761865e17a91b24e26c7ddbcba92459b5e83aaacffd5375f9bafe9d6","abstract_canon_sha256":"06b9a7bfe20e7b73a3799d1a59673ef0ea4adfa8d0a5883cfbc9ac7510f4e5bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:02.194637Z","signature_b64":"asAQHdnNmxXYj+PDGO1iR3t7uceU6+XviXaY8gozQrR1bqgNJXDAIq9RMfeF1eVIDnzcKE7whz62Vgfh4/yIBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"113f2fded8ffb5320a4e7feef02cf8d053b38af811481fb6d2aac6bea31485ba","last_reissued_at":"2026-07-05T11:15:02.193926Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:02.193926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"IP-Dialog: Evaluating Implicit Personalization in Dialogue Systems with Synthetic Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.HC"],"primary_cat":"cs.CL","authors_text":"Bo Peng, Chaochao Lu, Heyang Gong, Zhiheng Wang","submitted_at":"2025-06-03T05:14:11Z","abstract_excerpt":"In modern dialogue systems, the ability to implicitly infer user backgrounds from conversations and leverage this information for personalized assistance is crucial. However, the scarcity of high-quality data remains a fundamental challenge to evaluating and improving this capability. Traditional dataset construction methods are labor-intensive, resource-demanding, and raise privacy concerns. To address these issues, we propose a novel approach for automatic synthetic data generation and introduce the Implicit Personalized Dialogue (IP-Dialog) benchmark along with a training dataset, covering "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02449","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/2506.02449/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":"2506.02449","created_at":"2026-07-05T11:15:02.194042+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02449v1","created_at":"2026-07-05T11:15:02.194042+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02449","created_at":"2026-07-05T11:15:02.194042+00:00"},{"alias_kind":"pith_short_12","alias_value":"CE7S7XWY762T","created_at":"2026-07-05T11:15:02.194042+00:00"},{"alias_kind":"pith_short_16","alias_value":"CE7S7XWY762TECSO","created_at":"2026-07-05T11:15:02.194042+00:00"},{"alias_kind":"pith_short_8","alias_value":"CE7S7XWY","created_at":"2026-07-05T11:15:02.194042+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/CE7S7XWY762TECSOP7XPALHY2B","json":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B.json","graph_json":"https://pith.science/api/pith-number/CE7S7XWY762TECSOP7XPALHY2B/graph.json","events_json":"https://pith.science/api/pith-number/CE7S7XWY762TECSOP7XPALHY2B/events.json","paper":"https://pith.science/paper/CE7S7XWY"},"agent_actions":{"view_html":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B","download_json":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B.json","view_paper":"https://pith.science/paper/CE7S7XWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02449&json=true","fetch_graph":"https://pith.science/api/pith-number/CE7S7XWY762TECSOP7XPALHY2B/graph.json","fetch_events":"https://pith.science/api/pith-number/CE7S7XWY762TECSOP7XPALHY2B/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B/action/storage_attestation","attest_author":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B/action/author_attestation","sign_citation":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B/action/citation_signature","submit_replication":"https://pith.science/pith/CE7S7XWY762TECSOP7XPALHY2B/action/replication_record"}},"created_at":"2026-07-05T11:15:02.194042+00:00","updated_at":"2026-07-05T11:15:02.194042+00:00"}