用GAN和提示工程提升虚拟受害者模拟的真实性。
VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity
- 采用GAN对抗训练+关键信息提示，增强角色真实感。
- 人类评估显示，模型在拟人度上优于GPT-4。
- 适合警察、急救等需要情感共情训练的公共部门。
场景化训练已在多个公共服务领域广泛应用。近年来，大语言模型（LLMs）在模拟多样化人物方面展现出潜力，但如何构建适用于情景训练的虚拟受害者仍缺乏研究。本文提出VicSim（受害者模拟器），从信息忠实度、情绪动态和语言风格（如语法使用）三个维度提升用户模拟效果。创新性地结合基于生成对抗网络（GAN）的训练流程与关键信息提示策略，以增强模拟受害者的逼真度。通过对抗训练，判别器学习将语法和情绪线索作为合成内容的可靠指标。人类评估结果显示，VicSim在拟人度上优于GPT-4。
原文摘要 · Abstract (English)
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.
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