面向常识感知对话智能体的对话思维链蒸馏
Dialogue Chain-of-Thought Distillation for Commonsense-aware Conversational Agents
AI总结：
本文提出知识蒸馏框架，利用LLMs作为不可靠教师并通过一致性过滤器选择性蒸馏一致依据，构建对话思维链推理器DOCTOR，为对话智能体提供高质量推理依据以显著提升回复质量。
摘要：
类人聊天机器人需要运用常识推理，才能有效理解和回应对话中存在的隐含信息。然而，在回复中实现这种连贯性和信息量并非易事。即使对于大型语言模型（LLMs）来说，在单跳内识别和聚合关键证据也是一项重大挑战。这种复杂性源于此类证据分散在对话的多个轮次中，因此需要进行多跳整合。因此，我们的重点是促进基于对话上下文的此类多跳推理，即对话思维链推理。为此，我们提出了一个知识蒸馏框架，该框架将LLMs作为不可靠的教师，并通过一致性过滤器选择性地蒸馏出一致且有用的依据。我们进一步提出了DOCTOR，一个DialOgue Chain-of-ThOught Reasoner（对话思维链推理器），为回复生成提供可靠的CoT依据。我们进行了大量实验，结果表明，用DOCTOR的高质量依据增强对话智能体，能显著提高其回复质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
