用大模型模拟人格特质，构建测试框架与数据集
Exploring the Potential of Large Language Models to Simulate Personality
- 基于五大性格模型生成人格化文本，评估大模型表现
- 发现当前大模型在生成一致人格文本上仍存挑战
- 提供可复现的评测数据集与分析框架，适合对话系统研究者
随着大语言模型（LLMs）的发展，对话人工智能的重点已从生成连贯回应转向个性化交互。为提升用户参与度，聊天机器人常被设计为模仿人类行为，在特定情感范围内响应，并符合一套价值观。本文旨在利用大语言模型模拟基于五大性格模型（Big Five）的人格特质。研究发现，生成具有明确人格特征的文本仍是模型的难点。为此，我们构建了一个包含预设五大性格特征的生成文本数据集，并提出一个用于评估大模型人格模拟能力的分析框架。
原文摘要 · Abstract (English)
With the advancement of large language models (LLMs), the focus in Conversational AI has shifted from merely generating coherent and relevant responses to tackling more complex challenges, such as personalizing dialogue systems. In an effort to enhance user engagement, chatbots are often designed to mimic human behaviour, responding within a defined emotional spectrum and aligning to a set of values. In this paper, we aim to simulate personal traits according to the Big Five model with the use of LLMs. Our research showed that generating personality-related texts is still a challenging task for the models. As a result, we present a dataset of generated texts with the predefined Big Five characteristics and provide an analytical framework for testing LLMs on a simulation of personality skills.
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