VicSim：以情感与语言保真度增强受害者模拟
VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity
AI总结：
针对公共服务情景训练中的受害者模拟问题，提出VicSim模型，融合GAN训练与关键信息提示，提升信息、情感和语言保真度，并在人类评估中拟人度超过GPT-4。
摘要：
基于情景的训练已被许多公共服务部门广泛采用。大型语言模型（LLM）的最新进展在模拟多样化人物角色以创建这些训练情景方面展现出潜力。然而，关于如何开发LLM以模拟受害者来服务于基于情景的训练目的，目前所知甚少。本文介绍VicSim（victim simulator，受害者模拟器），这一新颖模型处理用户模拟的三个关键维度：信息保真度、情感动态和语言风格（例如语法使用）。我们率先将基于情景的受害者建模与基于GAN的训练流程及基于关键信息的提示相结合，旨在提高模拟受害者的真实感。我们的对抗训练方法教判别器将语法和情感线索识别为合成内容的可靠指标。根据人类评分者的评估，VicSim模型在拟人度方面优于GPT-4。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
