Learning Steerable Clarification Policies with Collaborative Self-play

This article, sourced from ArXiv, likely presents a novel approach to improving the performance of language models (LLMs) by focusing on clarification strategies. The use of "collaborative self-play" suggests a training method where models interact with each other to refine their ability to ask clarifying questions and understand ambiguous information. The title indicates a focus on making these clarification policies "steerable," implying control over the types of questions asked or the information sought. The research falls under the category of LLM research.