Traditionally, offline datasets have been used to evaluate task-oriented dialogue (TOD) models. These datasets lack context awareness, making them suboptimal benchmarks for conversational systems. In contrast, user-agents, which are context-aware, can simulate the variability and unpredictability of human conversations, making them better alternatives as evaluators. Prior research has utilized large language models (LLMs) to develop user-agents. Our work builds upon this by using LLMs to create user-agents for the evaluation of TOD systems. This involves prompting an LLM, using in-context examples as guidance, and tracking the user-goal state. Our evaluation of diversity and task completion metrics for the user-agents shows improved performance with the use of better prompts. Additionally, we propose methodologies for the automatic evaluation of TOD models within this dynamic framework.
@article{arxiv.2411.09972,
title = {Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems},
author = {Taaha Kazi and Ruiliang Lyu and Sizhe Zhou and Dilek Hakkani-Tur and Gokhan Tur},
journal= {arXiv preprint arXiv:2411.09972},
year = {2024}
}