Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
This addresses the reliability issue of LLMs for users in AI applications, but it is incremental as it builds on prior work on label bias.
The study tackled the problem of label bias in large language models (LLMs) by evaluating quantification methods across 279 classification tasks and ten models, revealing substantial bias even after debiasing attempts, and proposed a novel calibration method that outperformed recent approaches in improving performance and mitigating bias.
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However, recent work revealed they also exhibit label bias -- an undesirable preference toward predicting certain answers over others. Still, detecting and measuring this bias reliably and at scale has remained relatively unexplored. In this study, we evaluate different approaches to quantifying label bias in a model's predictions, conducting a comprehensive investigation across 279 classification tasks and ten LLMs. Our investigation reveals substantial label bias in models both before and after debiasing attempts, as well as highlights the importance of outcomes-based evaluation metrics, which were not previously used in this regard. We further propose a novel label bias calibration method tailored for few-shot prompting, which outperforms recent calibration approaches for both improving performance and mitigating label bias. Our results emphasize that label bias in the predictions of LLMs remains a barrier to their reliability.