用个人经历生成角色背景，让大模型更懂个体价值观。
ValueSim: Generating Backstories to Model Individual Value Systems
- 通过用户数据生成叙事性个人背景，模拟独特价值观。
- 在自建基准上准确率比检索增强方法高10%以上。
- 越多人机交互历史，模拟越精准，适合个性化对话系统。
随着大语言模型（LLMs）日益展现类人能力，使其与人类价值观对齐变得至关重要。现有先进方法如提示学习和强化学习虽能处理广泛伦理问题与助人属性，却极少关注个体化价值体系的建模。为此，我们提出ValueSim框架，通过生成反映过往经历与人口统计信息的个人叙事背景，模拟个体价值体系。该框架将结构化用户数据转化为叙述性背景，并采用受认知-情感人格系统启发的多模块架构，基于这些背景模拟个体价值观。在基于世界价值观调查自建基准上的测试表明，其在top-1准确率上相比检索增强生成方法提升超过10%。进一步分析显示，随着用户交互历史的增加，性能持续提升，表明模型具备随时间优化角色模拟的能力。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) continue to exhibit increasingly human-like capabilities, aligning them with human values has become critically important. Contemporary advanced techniques, such as prompt learning and reinforcement learning, are being deployed to better align LLMs with human values. However, while these approaches address broad ethical considerations and helpfulness, they rarely focus on simulating individualized human value systems. To address this gap, we present ValueSim, a framework that simulates individual values through the generation of personal backstories reflecting past experiences and demographic information. ValueSim converts structured individual data into narrative backstories and employs a multi-module architecture inspired by the Cognitive-Affective Personality System to simulate individual values based on these narratives. Testing ValueSim on a self-constructed benchmark derived from the World Values Survey demonstrates an improvement in top-1 accuracy by over 10% compared to retrieval-augmented generation methods. Further analysis reveals that performance enhances as additional user interaction history becomes available, indicating the model's ability to refine its persona simulation capabilities over time.
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