Asking Clarifying Questions for Preference Elicitation with Large Language Models

Large Language Models (LLMs) have made it possible for recommendation systems to interact with users in open-ended conversational interfaces. In order to personalize LLM responses, it is crucial to elicit user preferences, especially when there is limited user history. One way to get more information is to present clarifying questions to the user. However, generating effective sequential clarifying questions across various domains remains a challenge, as even advanced LLMs still struggle with this task.
To address this, we introduce a novel approach for training LLMs to ask sequential questions that reveal user preferences. Our method follows a two-stage process inspired by diffusion models: starting from a user profile, in a forward process we generate clarifying questions, obtain answers, and then remove the corresponding information from the user profile, which is analogous to adding noise to the user profile. In the reverse process, zour model learns to “denoise” the user profile by learning to ask effective clarifying questions. Our results show that our method significantly boosts the LLM’s proficiency in asking funnel questions and elicit user preferences effectively.