As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency. However, traditional user studies used to evaluate these tools are often costly, time consuming, and difficult to scale. In this paper, we explore the use of Large Language Models (LLMs) to replicate human participants to help streamline XAI evaluation. We reproduce a user study comparing counterfactual and causal explanations, replicating human participants with seven LLMs under various settings. Our results show that (i) LLMs can replicate most conclusions from the original study, (ii) different LLMs yield varying levels of alignment in the results, and (iii) experimental factors such as LLM memory and output variability affect alignment with human responses. These initial findings suggest that LLMs could provide a scalable and cost-effective way to simplify qualitative XAI evaluation.

本研究解决了传统用户研究在评估可解释人工智能工具方面成本高、耗时长、难以扩展的问题。我们提出利用大型语言模型（LLMs）替代人类参与者，进行关于对照和因果解释的比较研究。研究发现LLMs能够有效再现原研究的主要结论，且在可扩展性和成本效益方面展现出潜力，为定性可解释人工智能评估提供了一种新的解决方案。

通过大型语言模型评估解释：超越传统用户研究