DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines

Abstract

Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce nonengaging, unsafe responses. This unpredictability diminishes user trust and can hinder the use of the models in the real world. To address this, we introduce DIALGUIDE, a novel framework for controlling dialogue model behavior using natural language rules, or guidelines. These guidelines provide information about the context they are applicable to and what should be included in the response, allowing the models to be more closely aligned with the developer's expectations and intent. We evaluate DIAL-GUIDE on three tasks in open-domain dialogue response generation: guideline selection, response generation, and response entailment verification. Our dataset contains 10,737 positive and 15,467 negative dialogue context-responseguideline triplets across two domains -chitchat and safety. We provide baseline models for the tasks and benchmark their performance. Our results demonstrate that DIALGUIDE is effective in producing safe and engaging responses that follow developer guidelines.