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ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments

Ruolan Yang, Zitong Li, Haifeng Tang, Kenny Q. Zhu

2022Year
12Citations

Abstract

Existing automatic evaluation systems of chatbots mostly rely on static chat scripts as ground truth, which is hard to obtain, and requires access to the models of the bots as a form of “white-box testing”. Interactive evaluation mitigates this problem but requires human involvement. In our work, we propose an interactive chatbot evaluation framework in which chatbots compete with each other like in a sports tournament, using flexible scoring metrics. This framework can efficiently rank chatbots independently from their model architectures and the domains for which they are trained.

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