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VicSim: Enhancing Victim Simulation with Emotional and Linguistic Fidelity

2025-01-06 17:01:45
Yerong Li, Yiren Liu, Yun Huang

Abstract

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.

Abstract (translated)

情景式培训在许多公共服务领域被广泛采用。大型语言模型(LLM)的最新进展显示,它们能够模拟多样化的角色以创建这些培训场景,展现出了巨大潜力。然而,关于如何开发大型语言模型来为情境训练模拟受害者尚知之甚少。在此论文中,我们介绍了VicSim(受害人模拟器),这是一种新型模型,它在用户模拟的三个关键维度上进行了创新:信息真实性、情感动态和语言风格(例如语法使用)。我们开创了情景式受害人建模与基于生成对抗网络(GAN)训练工作流及基于关键信息提示的结合,旨在增强模拟受害者的真实感。我们的对抗性培训方法教导判别器识别语法和情绪线索作为合成内容可靠的指标。根据人类评估者的评价,VicSim模型在逼真度方面超越了GPT-4。

URL

https://arxiv.org/abs/2501.03139

PDF

https://arxiv.org/pdf/2501.03139.pdf


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