BEADs: Bias Evaluation Across Domains
This addresses the need for comprehensive bias evaluation in NLP, though it is incremental as it builds on existing bias detection efforts.
The authors tackled the problem of limited bias evaluation datasets in NLP by introducing the BEADs dataset, which supports diverse tasks and reveals systematic biases and inconsistent safety guardrails in state-of-the-art models.
Recent advances in large language models (LLMs) have substantially improved natural language processing (NLP) applications. However, these models often inherit and amplify biases present in their training data. Although several datasets exist for bias detection, most are limited to one or two NLP tasks, typically classification or evaluation and do not provide broad coverage across diverse task settings. To address this gap, we introduce the \textbf{Bias Evaluations Across Domains} (\textbf{B}\texttt{EADs}) dataset, designed to support a wide range of NLP tasks, including text classification, token classification, bias quantification, and benign language generation. A key contribution of this work is a gold-standard annotation scheme that supports both evaluation and supervised training of language models. Experiments on state-of-the-art models reveal some gaps: some models exhibit systematic bias toward specific demographics, while others apply safety guardrails more strictly or inconsistently across groups. Overall, these results highlight persistent shortcomings in current models and underscore the need for comprehensive bias evaluation. Project: https://vectorinstitute.github.io/BEAD/ Data: https://huggingface.co/datasets/shainar/BEAD