Knowledge retrieval is one of the major challenges in building a knowledge-grounded dialogue system. A common method is to use a neural retriever with a distributed approximate nearest-neighbor database to quickly find the relevant knowledge sentences. In this work, we propose an approach that utilizes topic modeling on the knowledge base to further improve retrieval accuracy and as a result, improve response generation. Additionally, we experiment with a large language model, ChatGPT, to take advantage of the improved retrieval performance to further improve the generation results. Experimental results on two datasets show that our approach can increase retrieval and generation performance. The results also indicate that ChatGPT is a better response generator for knowledge-grounded dialogue when relevant knowledge is provided.

翻译：知识检索是构建知识驱动型对话系统的主要挑战之一。常见方法是使用配备分布式近似最近邻数据库的神经检索器，快速找到相关知识语句。本文提出一种方法，通过对知识库进行主题建模来进一步提高检索准确性，从而改善响应生成质量。此外，我们利用大型语言模型ChatGPT进行实验，借助改进的检索性能进一步优化生成结果。在两个数据集上的实验结果表明，我们的方法能够提升检索和生成性能。同时，结果也表明在提供相关知识的情况下，ChatGPT是知识驱动型对话中更优的响应生成器。