LMLPA：语言模型语言人格评估
LMLPA: Language Model Linguistic Personality Assessment
AI总结：
本文提出LMLPA系统，通过改编Big Five Inventory为开放式问卷并利用AI评分器与主成分分析，定量评估大型语言模型的独特语言人格特质，为人机交互等领域提供稳健框架。
摘要：
大型语言模型（LLMs）在日常生活和研究中的应用日益广泛。最常见的用例之一是由LLMs语言生成能力支持的对话交互。正如两人之间一样，LLM驱动的实体与人类之间的对话也取决于对话者的人格。然而，衡量给定LLM的人格目前是一项挑战。本文介绍了语言模型语言人格评估（LMLPA），这是一个旨在评估LLMs语言人格的系统。该系统通过定量评估LLMs语言输出中反映的独特人格特质，帮助理解其语言生成能力。与传统的以人为中心的心理测量学不同，LMLPA改编了人格评估问卷（特别是Big Five Inventory），以适应LLMs的操作能力，并结合了以往基于语言的人格测量文献的发现。为降低对选项顺序的敏感性，问卷设计为开放式，从而产生文本答案。因此，需要AI评分器将文本回答中模糊的人格信息转化为清晰的人格特质数值指标。利用主成分分析和信度验证，研究结果表明LLMs具有独特的人格特质，且这些特质可被LMLPA有效量化。本研究对Human-Computer Interaction和Human-Centered AI有所贡献，为未来研究完善AI人格评估并扩展其在教育、制造等多个领域的应用提供了稳健的框架。
英文摘要：
Large Language Models (LLMs) are increasingly used in everyday life and research. One of the most common use cases is conversational interactions, enabled by the language generation capabilities of LLMs. Just as between two humans, a conversation between an LLM-powered entity and a human depends on the personality of the conversants. However, measuring the personality of a given LLM is currently a challenge. This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs. Our system helps to understand LLMs' language generation capabilities by quantitatively assessing the distinct personality traits reflected in their linguistic outputs. Unlike traditional human-centric psychometrics, the LMLPA adapts a personality assessment questionnaire, specifically the Big Five Inventory, to align with the operational capabilities of LLMs, and also incorporates the findings from previous language-based personality measurement literature. To mitigate sensitivity to the order of options, our questionnaire is designed to be open-ended, resulting in textual answers. Thus, the AI rater is needed to transform ambiguous personality information from text responses into clear numerical indicators of personality traits. Utilising Principal Component Analysis and reliability validations, our findings demonstrate that LLMs possess distinct personality traits that can be effectively quantified by the LMLPA. This research contributes to Human-Computer Interaction and Human-Centered AI, providing a robust framework for future studies to refine AI personality assessments and expand their applications in multiple areas, including education and manufacturing.
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。
