{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:37:38Z","timestamp":1790163458579,"version":"4.1.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["623B2097"],"award-info":[{"award-number":["623B2097"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"MCC Lab of Information Science and Technology Institution, USTC"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>\n                    Developing\n                    <jats:italic toggle=\"yes\">proactive social influence<\/jats:italic>\n                    dialogue systems presents a significant challenge, particularly in\n                    <jats:italic toggle=\"yes\">non-cooperative<\/jats:italic>\n                    scenarios where the system\u2019s goals may conflict with those of the user. Traditional methods often focus on training models to plan dialogue strategies, but since human strategies are often sub-optimal, relying solely on manually collected data can be problematic. While\n                    <jats:bold>Large Language Models (LLMs)<\/jats:bold>\n                    facilitate the generation of high-quality synthetic dialogues, their effectiveness in strategic dialogue under zero-shot or few-shot conditions is inconsistent. To address these issues, we propose a training framework applicable to multiple social influence dialogue tasks, named\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    <jats:bold>ationale-Enhanced and<\/jats:bold>\n                    <jats:italic toggle=\"yes\">R<\/jats:italic>\n                    <jats:bold>eward Model-Based<\/jats:bold>\n                    <jats:italic toggle=\"yes\">E<\/jats:italic>\n                    <jats:bold>nd-to-End<\/jats:bold>\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    <jats:bold>ocial<\/jats:bold>\n                    <jats:italic toggle=\"yes\">I<\/jats:italic>\n                    <jats:bold>nfluence Dialogue<\/jats:bold>\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    <jats:bold>ys<\/jats:bold>\n                    <jats:italic toggle=\"yes\">t<\/jats:italic>\n                    <jats:bold>em (RESIST)<\/jats:bold>\n                    . To streamline the dialogue system development, we first use existing datasets to prompt a teacher LLM for generating \u201cchain-of-thought\u201d rationales, which are then used to enrich the data and enable\n                    <jats:bold>Supervised Fine-Tuning (SFT)<\/jats:bold>\n                    of the model. Next, we train a reward model by ranking the fine-tuned model\u2019s outputs, thereby deriving task-specific preferences without manually constructing scalar rewards. Finally, we apply reinforcement learning to further refine the system, optimizing dialogue strategies and responses according to specific tasks and conversational contexts. Experimental results on three social influence tasks demonstrate the effectiveness and adaptability of our training approach. In terms of\n                    <jats:italic toggle=\"yes\">task goal completion<\/jats:italic>\n                    , RESIST outperforms baseline models and even exceeds the performance of ChatGPT-driven prompt-based policy planning methods in both efficiency and effectiveness. Additionally, we introduce\n                    <jats:italic toggle=\"yes\">strategic proactivity<\/jats:italic>\n                    as a novel evaluation metric, enabling us to analyze how RESIST training influences the proactive traits of dialogue agents, with a particular focus on the personality tendencies of smaller-scale language models during task execution. Experimental findings indicate that RESIST enhances the strategic proactivity of language models, aligning them more closely with task requirements. The source code will be made publicly available upon publication.\n                  <\/jats:p>","DOI":"10.1145\/3736580","type":"journal-article","created":{"date-parts":[[2025,5,21]],"date-time":"2025-05-21T12:02:46Z","timestamp":1747828966000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["RESIST: Rationale-Enhanced and Reward Model-Based End-to-End Social Influence Dialogue System"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5870-4556","authenticated-orcid":false,"given":"Tong","family":"Wu","sequence":"first","affiliation":[{"name":"AHU-IAI AI Joint Laboratory, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-9077","authenticated-orcid":false,"given":"Jinhua","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1690-9836","authenticated-orcid":false,"given":"Wengang","family":"Zhou","sequence":"additional","affiliation":[{"name":"EEIS Department, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2188-3028","authenticated-orcid":false,"given":"Houqiang","family":"Li","sequence":"additional","affiliation":[{"name":"EEIS Department, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,7]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"1","volume-title":"Proceedings of the Annual Meeting of the Association for Computational Linguistics","author":"Deng Yang","year":"2023","unstructured":"Yang Deng, Wenqiang Lei, Minlie Huang, and Tat-Seng Chua. 2023. 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