Scenario-based training has been widely adopted in many public service sectors. Recent advancements in Large Language Models (LLMs) have shown promise in simulating diverse personas to create these training scenarios. However, little is known about how LLMs can be developed to simulate victims for scenario-based training purposes. In this paper, we introduce VicSim (victim simulator), a novel model that addresses three key dimensions of user simulation: informational faithfulness, emotional dynamics, and language style (e.g., grammar usage). We pioneer the integration of scenario-based victim modeling with GAN-based training workflow and key-information-based prompting, aiming to enhance the realism of simulated victims. Our adversarial training approach teaches the discriminator to recognize grammar and emotional cues as reliable indicators of synthetic content. According to evaluations by human raters, the VicSim model outperforms GPT-4 in terms of human-likeness.

本研究解决了当前场景训练中缺乏对受害者模拟的有效方法的问题。我们提出了VicSim，一个新模型，通过整合场景化受害者建模与基于生成对抗网络的训练工作流，提升了模拟受害者的真实感和人类相似度。评估结果显示，VicSim模型在模拟人类特征方面优于GPT-4，具有显著的应用潜力。

VicSim：通过情感和语言忠实度增强受害者模拟