Simulating User Diversity in Task-Oriented Dialogue Systems using Large Language Models

• URL: http://arxiv.org/abs/2502.12813v1
• Date: Tue, 18 Feb 2025 12:20:16 GMT
• Title: Simulating User Diversity in Task-Oriented Dialogue Systems using Large Language Models
• Authors: Adnan Ahmad, Stefan Hillmann, Sebastian Möller,
• Abstract summary: We employ two proprietary Large Language Models (LLMs) to generate a heterogeneous base of user profiles. We perform a detailed analysis of the user profiles generated by LLMs to assess the diversity, consistency, and potential biases inherent in these simulations. We find that GPT-o1 generates more heterogeneous user distribution across most user attributes, while GPT-4o generates more skewed user attributes.
• Score: 11.708400514900053
• License:
• Abstract: In this study, we explore the application of Large Language Models (LLMs) for generating synthetic users and simulating user conversations with a task-oriented dialogue system and present detailed results and their analysis. We propose a comprehensive novel approach to user simulation technique that uses LLMs to create diverse user profiles, set goals, engage in multi-turn dialogues, and evaluate the conversation success. We employ two proprietary LLMs, namely GPT-4o and GPT-o1 (Achiam et al., 2023), to generate a heterogeneous base of user profiles, characterized by varied demographics, multiple user goals, different conversational styles, initial knowledge levels, interests, and conversational objectives. We perform a detailed analysis of the user profiles generated by LLMs to assess the diversity, consistency, and potential biases inherent in these LLM-generated user simulations. We find that GPT-o1 generates more heterogeneous user distribution across most user attributes, while GPT-4o generates more skewed user attributes. The generated set of user profiles are then utilized to simulate dialogue sessions by interacting with a task-oriented dialogue system.

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