ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments

ACL 2022  ·  Ruolan Yang, Zitong Li, Haifeng Tang, Kenny Zhu ·

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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