Evaluating LLMs for Gender Disparities in Notable Persons

arXiv:2403.09148v1 Announce Type: new
Abstract: This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This paper takes a multi-pronged approach to evaluating GPT models by evaluating fairness across multiple dimensions of recall, hallucinations and declinations. Our findings reveal discernible gender …

abstract arxiv biases concerns cs.cl cs.ir gender information language language models large language large language models llms prompt responses study type