Taeyoon Kwon
2023
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
Hyungjoo Chae
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Yongho Song
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Kai Ong
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Taeyoon Kwon
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Minjin Kim
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Youngjae Yu
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Dongha Lee
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Dongyeop Kang
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Jinyoung Yeo
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
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.
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Co-authors
- Hyungjoo Chae 1
- Yongho Song 1
- Kai Ong 1
- Minjin Kim 1
- Youngjae Yu 1
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