{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:LWMXRALZSO7GQPZXCWUARPSOG4","short_pith_number":"pith:LWMXRALZ","schema_version":"1.0","canonical_sha256":"5d9978817993be683f3715a808be4e3714145bdd09647bbf9c041505f0b8b450","source":{"kind":"arxiv","id":"2412.05342","version":5},"attestation_state":"computed","paper":{"title":"Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Luo Ji, Ningyuan Xi, Qingqing Gu, Teng Chen, Xiaokai Chen, Xiaoyu Wang, Yong Chen, Yue Zhao, Zhonglin Jiang","submitted_at":"2024-12-06T09:33:47Z","abstract_excerpt":"Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication. Previous LLM-based researches mainly focus on the multi-agent framework, while their base LLMs are still pairwisely fine-tuned. In this work, we design a multi-party fine-tuning framework (MuPaS) for LLMs on the multi-party dialogue datasets, and prove such a straightforward framework can let the LLM align with the mul"},"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":"2412.05342","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-06T09:33:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f9070819f3a91526af665ad8e841ab00944f02211ad0af436625c3eab54370d9","abstract_canon_sha256":"962efd93b4e0b1e89f195417a2a2f3a766edcae4d927926237ec707efb12fcec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:19:22.986279Z","signature_b64":"NqVgSfC3ufb2qhtnAcchoFn3R3RRqRbagRZyTmQeTRi5SHlK2EKmW5ONOC3rRhqNYzDSu+flTtSY1+NY+cSVBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d9978817993be683f3715a808be4e3714145bdd09647bbf9c041505f0b8b450","last_reissued_at":"2026-07-05T11:19:22.984616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:19:22.984616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Party Supervised Fine-tuning of Language Models for Multi-Party Dialogue Generation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Luo Ji, Ningyuan Xi, Qingqing Gu, Teng Chen, Xiaokai Chen, Xiaoyu Wang, Yong Chen, Yue Zhao, Zhonglin Jiang","submitted_at":"2024-12-06T09:33:47Z","abstract_excerpt":"Large Language Models (LLM) are usually fine-tuned to participate in dyadic or two-party dialogues, which can not adapt well to multi-party dialogues (MPD), which hinders their applications in such scenarios including multi-personal meetings, discussions and daily communication. Previous LLM-based researches mainly focus on the multi-agent framework, while their base LLMs are still pairwisely fine-tuned. In this work, we design a multi-party fine-tuning framework (MuPaS) for LLMs on the multi-party dialogue datasets, and prove such a straightforward framework can let the LLM align with the mul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.05342","kind":"arxiv","version":5},"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/2412.05342/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":"2412.05342","created_at":"2026-07-05T11:19:22.984670+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.05342v5","created_at":"2026-07-05T11:19:22.984670+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.05342","created_at":"2026-07-05T11:19:22.984670+00:00"},{"alias_kind":"pith_short_12","alias_value":"LWMXRALZSO7G","created_at":"2026-07-05T11:19:22.984670+00:00"},{"alias_kind":"pith_short_16","alias_value":"LWMXRALZSO7GQPZX","created_at":"2026-07-05T11:19:22.984670+00:00"},{"alias_kind":"pith_short_8","alias_value":"LWMXRALZ","created_at":"2026-07-05T11:19:22.984670+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/LWMXRALZSO7GQPZXCWUARPSOG4","json":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4.json","graph_json":"https://pith.science/api/pith-number/LWMXRALZSO7GQPZXCWUARPSOG4/graph.json","events_json":"https://pith.science/api/pith-number/LWMXRALZSO7GQPZXCWUARPSOG4/events.json","paper":"https://pith.science/paper/LWMXRALZ"},"agent_actions":{"view_html":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4","download_json":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4.json","view_paper":"https://pith.science/paper/LWMXRALZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.05342&json=true","fetch_graph":"https://pith.science/api/pith-number/LWMXRALZSO7GQPZXCWUARPSOG4/graph.json","fetch_events":"https://pith.science/api/pith-number/LWMXRALZSO7GQPZXCWUARPSOG4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4/action/storage_attestation","attest_author":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4/action/author_attestation","sign_citation":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4/action/citation_signature","submit_replication":"https://pith.science/pith/LWMXRALZSO7GQPZXCWUARPSOG4/action/replication_record"}},"created_at":"2026-07-05T11:19:22.984670+00:00","updated_at":"2026-07-05T11:19:22.984670+00:00"}