The Career Interests of Large Language Models

TL;DR

This paper explores the hypothetical career interests of Large Language Models using psychometric tools, revealing human-like preferences that do not necessarily match their actual competencies, thus informing their potential professional roles.

Contribution

It introduces a novel method of assessing LLMs' career interests using psychometric instruments and statistical analysis, providing insights into their human-like tendencies and professional integration.

Findings

LLMs show preferences for social and artistic careers.

Preferences do not align with actual competence levels.

Highlights potential for human-like self-perception in LLMs.

Abstract

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.