Reevaluating Bias Detection in Language Models: The Role of Implicit Norm

arXiv:2404.03471v1 Announce Type: cross
Abstract: Large language models (LLMs), trained on vast datasets, can carry biases that manifest in various forms, from overt discrimination to implicit stereotypes. One facet of bias is performance disparities in LLMs, often harming underprivileged groups, such as racial minorities. A common approach to quantifying bias is to use template-based bias probes, which explicitly state group membership (e.g. White) and evaluate if the outcome of a task, sentiment analysis for instance, is invariant to the change …

abstract arxiv bias biases cs.cl cs.cy cs.lg datasets detection discrimination facet forms language language models large language large language models llms manifest norm performance racial role stereotypes type vast