Recent advancements in Large Language Models (LLMs) have significantly extended their capabilities, evolving from basic text generation to complex, human-like interactions. In light of the possibilities that LLMs could assume significant workplace responsibilities, it becomes imminently necessary to explore LLMs' capacities as professional assistants. This study focuses on the aspect of career interests by applying the Occupation Network's Interest Profiler short form to LLMs as if they were human participants and investigates their hypothetical career interests and competence, examining how these vary with language changes and model advancements. We analyzed the answers using a general linear mixed model approach and found distinct career interest inclinations among LLMs, particularly towards the social and artistic domains. Interestingly, these preferences did not align with the occupations where LLMs exhibited higher competence. This novel approach of using psychometric instruments and sophisticated statistical tools on LLMs unveils fresh perspectives on their integration into professional environments, highlighting human-like tendencies and promoting a reevaluation of LLMs' self-perception and competency alignment in the workforce.

翻译：近期，大型语言模型（LLMs）的能力取得了显著进展，已从基础的文本生成发展到复杂、类人的交互。鉴于LLMs可能承担重要工作职责的潜力，探索其作为专业助手的能力变得尤为迫切。本研究聚焦于职业兴趣方面，将职业网络兴趣分析器简表应用于LLMs，视其为人类参与者，并调查其假设的职业兴趣与能力，同时考察这些因素如何随语言变化和模型发展而产生差异。我们采用广义线性混合模型方法对回答进行了分析，发现LLMs存在明显的职业兴趣倾向，尤其偏好社会和艺术领域。有趣的是，这些偏好与LLMs表现出较高能力的职业并不一致。这种将心理测量工具和复杂统计方法应用于LLMs的新颖研究路径，为将其融入专业环境提供了全新视角，揭示了其类人化倾向，并促使我们重新审视LLMs在职场中的自我认知与能力匹配问题。