This paper aims to efficiently enable large language models (LLMs) to use external knowledge and goal guidance in conversational recommender system (CRS) tasks. Advanced LLMs (e.g., ChatGPT) are limited in domain-specific CRS tasks for 1) generating grounded responses with recommendation-oriented knowledge, or 2) proactively leading the conversations through different dialogue goals. In this work, we first analyze those limitations through a comprehensive evaluation, showing the necessity of external knowledge and goal guidance which contribute significantly to the recommendation accuracy and language quality. In light of this finding, we propose a novel ChatCRS framework to decompose the complex CRS task into several sub-tasks through the implementation of 1) a knowledge retrieval agent using a tool-augmented approach to reason over external Knowledge Bases and 2) a goal-planning agent for dialogue goal prediction. Experimental results on two multi-goal CRS datasets reveal that ChatCRS sets new state-of-the-art benchmarks, improving language quality of informativeness by 17% and proactivity by 27%, and achieving a tenfold enhancement in recommendation accuracy.

翻译：本文旨在高效赋能大语言模型（LLMs）在对话推荐系统（CRS）任务中利用外部知识与目标引导。先进的大语言模型（如ChatGPT）在领域特定的CRS任务中存在局限性，具体表现为：1）生成包含推荐相关知识的可靠回复，或2）通过不同对话目标主动引导对话进程。本研究首先通过全面评估分析上述局限性，表明外部知识与目标引导对提升推荐准确性与语言质量具有显著必要性。基于此发现，我们提出新型ChatCRS框架，通过以下方式将复杂CRS任务分解为若干子任务：1）构建知识检索智能体，采用增强工具的方法对外部知识库进行推理；2）设计目标规划智能体以预测对话目标。在两组多目标CRS数据集上的实验表明，ChatCRS刷新了现有最优基准，将语言信息丰富度提升17%，主动引导能力提升27%，推荐准确性实现十倍增幅。