對話系統中的自適應多代理回應精煉

摘要

大型語言模型（LLMs）在對話系統中展現出卓越成效，能夠生成類人回應。然而，當需要考量個人化或特定知識時，此類模型仍存在不足。在實際應用場景中，依賴用戶自行偵測錯誤並要求重新生成回應並不現實。解決此問題的一種方法是在回傳給用戶前對回應進行優化。現有方法多聚焦於單一大型語言模型內部的回應優化，但難以兼顧有效對話所需的多重面向。本研究提出透過多智能體框架進行回應優化，每個智能體分別負責特定面向的審核與改進。我們聚焦於對話品質的三個關鍵面向：事實性、個人化與連貫性。各智能體專責審核並優化其中一項面向，其回饋意見將被整合以提升整體回應品質。為強化智能體間的協作，我們引入動態通訊策略。有別於固定順序的智能體調用模式，本方法能根據每個查詢的具體需求，自適應地選擇並協調最相關的智能體。我們在具挑戰性的對話資料集上驗證此框架，結果顯示本方法顯著優於相關基線模型，尤其在涉及知識、用戶畫像或兩者兼具的任務中表現突出。

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.

對話系統中的自適應多代理回應精煉

Adaptive Multi-Agent Response Refinement in Conversational Systems

摘要

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