Realistic user simulation is crucial for training and evaluating task-oriented dialogue (TOD) systems, yet creating simulators that accurately replicate human behavior remains challenging. A key property of effective simulators is their ability to expose failure modes of the systems they evaluate. We present an adversarial training framework that iteratively improves user simulator realism through a competitive dynamic between a generator (user simulator) and a discriminator. Applied to mental health support chatbots, our approach demonstrates that fine-tuned simulators dramatically outperform zero-shot base models at surfacing system issues, and adversarial training further enhances diversity, distributional alignment, and predictive validity. The resulting simulator achieves a strong correlation between simulated and real failure occurrence rates across diverse chatbot configurations while maintaining low distributional divergence of failure modes. Discriminator accuracy decreases drastically after three adversarial iterations, suggesting improved realism. These results provide evidence that adversarial training is a promising approach for creating realistic user simulators in mental health support TOD domains, enabling rapid, reliable, and cost-effective system evaluation before deployment.

翻译：逼真的用户模拟对于训练和评估任务导向型对话系统至关重要，然而创建能够准确复现人类行为的模拟器仍具挑战性。有效模拟器的一个关键特性在于其能够暴露所评估系统的故障模式。本文提出一种对抗训练框架，通过生成器（用户模拟器）与判别器之间的竞争动态，迭代提升用户模拟器的真实性。将该方法应用于心理健康支持聊天机器人，我们的研究表明：经过微调的模拟器在揭示系统问题方面显著优于零样本基础模型，而对抗训练进一步增强了多样性、分布对齐性和预测有效性。所得模拟器在不同聊天机器人配置下，实现了模拟故障发生率与实际故障发生率之间的强相关性，同时保持了故障模式的低分布散度。经过三次对抗迭代后判别器准确率急剧下降，表明模拟真实性得到提升。这些结果证明，对抗训练是创建心理健康支持任务导向型对话领域中真实用户模拟器的有效途径，能够在系统部署前实现快速、可靠且经济高效的评估。