COBIAS: Contextual Reliability in Bias Assessment

arXiv:2402.14889v1 Announce Type: new
Abstract: Large Language Models (LLMs) are trained on inherently biased data. Previous works on debiasing models rely on benchmark datasets to measure model performance. However, these datasets suffer from several pitfalls due to the extremely subjective understanding of bias, highlighting a critical need for contextual exploration. We propose understanding the context of user inputs with consideration of the diverse situations in which input statements are possible. This approach would allow for frameworks that foster bias awareness …

abstract arxiv assessment benchmark bias biased data cs.ai cs.cl data datasets exploration highlighting language language models large language large language models llms performance reliability type understanding