{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dialogue-chain-of-thought-distillation-for","title":"Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents","arxiv_id":"2310.09343","date":"2023-10-13","proceeding":null,"authors":["Hyungjoo Chae","Yongho Song","Kai Tzu-iunn Ong","Taeyoon Kwon","Minjin Kim","Youngjae Yu","Dongha Lee","Dongyeop Kang","Jinyoung Yeo"],"abstract":"Human-like chatbots necessitate the use of commonsense reasoning in order to effectively comprehend and respond to implicit information present within conversations. Achieving such coherence and informativeness in responses, however, is a non-trivial task. Even for large language models (LLMs), the task of identifying and aggregating key evidence within a single hop presents a substantial challenge. This complexity arises because such evidence is scattered across multiple turns in a conversation, thus necessitating integration over multiple hops. Hence, our focus is to facilitate such multi-hop reasoning over a dialogue context, namely dialogue chain-of-thought (CoT) reasoning. To this end, we propose a knowledge distillation framework that leverages LLMs as unreliable teachers and selectively distills consistent and helpful rationales via alignment filters. We further present DOCTOR, a DialOgue Chain-of-ThOught Reasoner that provides reliable CoT rationales for response generation. We conduct extensive experiments to show that enhancing dialogue agents with high-quality rationales from DOCTOR significantly improves the quality of their responses.","url_abs":"https://arxiv.org/abs/2310.09343v2","url_pdf":"https://arxiv.org/pdf/2310.09343v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dialogue-chain-of-thought-distillation-for","repo_url":"https://github.com/kyle8581/dialoguecot","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"informativeness","task_name":"Informativeness"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"response-generation","task_name":"Response Generation"}],"methods":[{"method_slug":"focus","method_name":"Focus"},{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.09343","atlas_url":"https://app.syntology.ai/?focus=2310.09343","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.09343"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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