Identifying Multiple Personalities in Large Language Models with External Evaluation

arXiv:2402.14805v1 Announce Type: new
Abstract: As Large Language Models (LLMs) are integrated with human daily applications rapidly, many societal and ethical concerns are raised regarding the behavior of LLMs. One of the ways to comprehend LLMs' behavior is to analyze their personalities. Many recent studies quantify LLMs' personalities using self-assessment tests that are created for humans. Yet many critiques question the applicability and reliability of these self-assessment tests when applied to LLMs. In this paper, we investigate LLM personalities using …

abstract analyze applications arxiv assessment behavior concerns cs.ai cs.cl daily ethical evaluation human language language models large language large language models llms multiple personalities studies type