Beyond Performance: Quantifying and Mitigating Label Bias in LLMs

TL;DR

This paper investigates label bias in large language models across numerous tasks, evaluates existing detection methods, and introduces a new calibration technique that improves bias mitigation and model reliability.

Contribution

It provides a comprehensive analysis of label bias in LLMs, evaluates detection approaches, and proposes a novel calibration method tailored for few-shot prompting.

Findings

Substantial label bias exists in LLM predictions both before and after debiasing.

Outcome-based evaluation metrics are crucial for measuring label bias.

The proposed calibration method outperforms existing approaches in bias mitigation.

Abstract

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.