Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications

arXiv:2310.14607v2 Announce Type: replace-cross
Abstract: Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks. However, LLMs have been shown to exhibit harmful social biases that reflect the stereotypes and inequalities present in society. To this end, as well as the widespread use of tabular data in many high-stake applications, it is important to explore the following questions: what sources of information do LLMs draw upon when making classifications for tabular tasks; …

abstract arxiv biases cs.cl cs.lg fairness however language language models large language large language models literature llms social society stereotypes tabular tasks type