研究评估 GPT-4、Gemma 等模型能否模拟个人人格问卷回答

结果显示，提供用户文本并未稳定提升预测效果。模型在特质分布中段表现较好，但在分布两端往往停留在中心值，或在一端表现良好、另一端表现很差。在只给显式人格画像的条件下，GPT-4 最能将特质信息转化为一致的问卷回答，尤其在 Extraversion 和 Neuroticism 维度上。增加上下文文本长度也没有系统性降低误差：开源模型波动较大，GPT-4 相对稳定。

这项工作对“数字孪生”式个性化模拟提出了量化质疑：当前主流模型尚不能稳定复现具体个体的问卷行为，做用户模拟或社会科学实验的研究者需要谨慎对待这类应用。

Large Language Models as Digital Twins: a Distribution-Aware Questionnaire-Based Evaluation

This paper investigates whether Large Language Models can answer a personality questionnaire as a specific individual would. Given a user's social-media posts, the model completes the BFI-2-60 Big Five questionnaire as that user. The predicted responses are converted into trait scores and compared with the user's actual scores. We evaluate GPT-4, Gemma, Llama 3.1, and Mistral on the MyPersonality dataset. To determine whether predictions reflect information from the user's writing, we compare them with answers produced without user text and with values derived from general population tendencies. We measure prediction error across all users and separately for individuals with low, intermediate, and high trait scores. We also test whether an LLM can express an explicit personality profile when given only the target Big Five scores, without user text, and examine how predictions change as more text is provided. The results show that user text does not consistently improve performance. Models perform better near the middle of the trait distribution, while at the lower and upper ends they often remain close to central values or perform well at one end but poorly at the other. When given an explicit profile, GPT-4 most clearly translates trait information into consistent questionnaire responses, particularly for Extraversion and Neuroticism. Increasing context does not systematically reduce error: open-source models fluctuate more, whereas GPT-4 remains comparatively stable. Overall, the evaluated models do not consistently reproduce the questionnaire behavior of specific individuals.

本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：GPT-4、Llama 3.1、Gemma、Mistral，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Francesco Di Cursi, Chiara Boldrini, Andrea Passarella, Marco Conti），经 AITOP 多维评分管线结构化提炼。截至目前，全网已有 2 家独立信源对该事件进行交叉印证与跟进报道（含 rohanpaul_ai 等），属于近期高关注度行业信号。