Bot-Adversarial Dialogue for Safe Conversational Agents

Abstract

Warning: this paper contains example data that may be offensive or upsetting. Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which may include offensive or otherwise toxic behavior. We introduce a new human-and-model-in-the-loop framework for evaluating the toxicity of such models, and compare a variety of existing methods in both the cases of non-adversarial and adversarial users that expose their weaknesses. We then go on to propose two novel methods for safe conversational agents, by either training on data from our new human-and-model-in-the-loop framework in a two-stage system, or “baking-in” safety to the generative model itself. We find our new techniques are (i) safer than existing models; while (ii) maintaining usability metrics such as engagingness relative to state-of-the-art chatbots. In contrast, we expose serious safety issues in existing standard systems like GPT2 (Radford et al., 2019), DialoGPT (Zhang et al., 2019) and BlenderBot (Roller et al., 2020).