对话系统中自适应多智能体响应优化

摘要

大型语言模型（LLM）在对话系统中通过生成类人回复取得了显著成功。然而，当需要兼顾个性化或特定知识时，其表现仍存在不足。在实际应用场景中，依赖用户发现错误并请求重新生成回复并不现实。解决该问题的一种方法是在返回回复前对其进行优化。现有方法主要聚焦于单一LLM内部的回复优化，但难以兼顾有效对话所需的多样化维度。本研究提出通过多智能体框架优化回复，每个智能体被分配特定角色以处理不同维度。我们重点关注对话质量的三个关键维度：事实性、个性化与连贯性。每个智能体负责审查并优化其中一个维度，其反馈意见最终被整合以提升整体回复质量。为增强智能体间的协作，我们引入了动态通信策略。该方法并非遵循固定的智能体执行顺序，而是根据每个查询的具体需求自适应地选择并协调最相关的智能体。我们在具有挑战性的对话数据集上验证了该框架，结果表明本方法显著优于相关基线模型，尤其在涉及知识或用户画像的任务中表现突出。

English

Large Language Models (LLMs) have demonstrated remarkable success in conversational systems by generating human-like responses. However, they can fall short, especially when required to account for personalization or specific knowledge. In real-life settings, it is impractical to rely on users to detect these errors and request a new response. One way to address this problem is to refine the response before returning it to the user. While existing approaches focus on refining responses within a single LLM, this method struggles to consider diverse aspects needed for effective conversations. In this work, we propose refining responses through a multi-agent framework, where each agent is assigned a specific role for each aspect. We focus on three key aspects crucial to conversational quality: factuality, personalization, and coherence. Each agent is responsible for reviewing and refining one of these aspects, and their feedback is then merged to improve the overall response. To enhance collaboration among them, we introduce a dynamic communication strategy. Instead of following a fixed sequence of agents, our approach adaptively selects and coordinates the most relevant agents based on the specific requirements of each query. We validate our framework on challenging conversational datasets, demonstrating that ours significantly outperforms relevant baselines, particularly in tasks involving knowledge or user's persona, or both.

对话系统中自适应多智能体响应优化

Adaptive Multi-Agent Response Refinement in Conversational Systems

摘要

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