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arXiv 2305.02547cs.CLcs.AIcs.HC

PersonaLLM:探究大语言模型表达人格特质的能力

PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits

Massachusetts Institute of Technology(麻省理工学院);Stanford University(斯坦福大学)

Hang Jiang, Xiajie Zhang, Xubo Cao, Cynthia Breazeal, Deb Roy, Jad Kabbara

AI总结:

本文通过让GPT-3.5和GPT-4模拟大五人格并完成BFI测试与写作任务,验证了LLM能一致表达指定人格特质,且人类可感知部分特质,但得知AI作者后准确率下降。

摘要:

尽管大语言模型(LLMs)在创建个性化聊天机器人方面有许多应用场景,但关于评估个性化LLMs的行为在多大程度上准确且一致地反映特定人格特质的研究仍然有限。我们考虑研究基于LLM的智能体的行为,并将其称为LLM人格(LLM personas),并以GPT-3.5和GPT-4为例进行案例研究,以探究LLMs是否能够生成与其被指定的人格画像相一致的内容。为此,我们基于大五人格模型模拟不同的人格LLM,让它们完成44项大五人格量表(BFI)人格测试和一项故事写作任务,然后通过自动评估和人工评估对其文章进行评价。结果表明,人格LLM自我报告的BFI分数与其被指定的人格类型一致,在五个人格特质上均观察到较大的效应量。此外,与人类写作语料库相比,人格LLM的写作呈现出与人格特质相关的代表性语言模式。进一步的人工评估显示,人类能够以高达80%的准确率感知某些人格特质。有趣的是,当标注者被告知作者为AI时,准确率显著下降。

英文摘要:

Despite the many use cases for large language models (LLMs) in creating personalized chatbots, there has been limited research on evaluating the extent to which the behaviors of personalized LLMs accurately and consistently reflect specific personality traits. We consider studying the behavior of LLM-based agents which we refer to as LLM personas and present a case study with GPT-3.5 and GPT-4 to investigate whether LLMs can generate content that aligns with their assigned personality profiles. To this end, we simulate distinct LLM personas based on the Big Five personality model, have them complete the 44-item Big Five Inventory (BFI) personality test and a story writing task, and then assess their essays with automatic and human evaluations. Results show that LLM personas' self-reported BFI scores are consistent with their designated personality types, with large effect sizes observed across five traits. Additionally, LLM personas' writings have emerging representative linguistic patterns for personality traits when compared with a human writing corpus. Furthermore, human evaluation shows that humans can perceive some personality traits with an accuracy of up to 80%. Interestingly, the accuracy drops significantly when the annotators were informed of AI authorship.

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。

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