Clippers 11/21: Lifeng Jin (Tencent) on safety and consistency in dialogue systems

Safety and Consistency in dialogue systems

Safety and consistency of generated utterances from dialogue systems have been important issues for dialogue system development. A good dialogue system should be safe all the time, even when provoked by users, and consistent with the context, even when the user is not. In this talk, I am going to present our attempts at addressing some of the issues related to safety and consistency with two new datasets, new tasks and experiments. Different models, including large language models such as ChatGPT and GPT4, are used in evaluation of tasks such as safe rewriting and inconsistency resolution to look at their ability to detect and amend dialogues caused by unsafe or inconsistent responses. I will discuss how they behave and what future directions are for these problems.