Evaluating Retrieval-Based Chatbot Systems: A Focus on Speed and Performance

This paper explores the design and evaluation of retrieval-based chatbots, emphasizing practical performance indicators and inference speed in industrial applications. We begin by outlining the structure of retrieval-based dialog systems, including fine-grained ranking and coarse-grained candidate set generation. Experiments are conducted using the Douban dataset to assess the effectiveness of the Faiss library in optimizing retrieval speeds. We present comparative results of state-of-the-art methods, followed by a critical analysis of the challenges and future trends in retrieval-based systems, particularly focusing on semantic matching, inference speed, and scaling to large datasets.