Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy

TL;DR

This paper introduces a post-hoc probability reweighting method for large language models to improve class fairness and accuracy by directly optimizing for fairness metrics without altering the model's internal weights.

Contribution

It proposes a novel discrete optimization approach with a new fairness metric, COBias, and an efficient metaheuristic solution for equitable LLM inference.

Findings

61% reduction in COBias disparity

18% increase in overall accuracy

Robust generalization across prompt configurations

Abstract

Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypical associations mirroring human cognitive biases). This leads to persistent, uneven class accuracy in classification and QA. Such per-class accuracy disparities are not inherently resolved by architectural/training evolutions or data scaling, making post-hoc correction essential for equitable performance. To mitigate LLM class accuracy imbalance, we develop a post-hoc probability reweighting method that directly optimizes for non-differentiable performance-driven and fairness-aligned metrics, through a novel COBias metric that highlights disparities in class accuracies. This post-hoc bias mitigation method is grounded in discrete optimization with nonlinear integer programming (NIP) objectives and an efficient metaheuristic solution framework with theoretical convergence guarantees. Operating model-agnostically, it learns reweighting coefficients from output class probabilities to adjust LLM inference outputs without internal weight updates. Evaluations demonstrate its effectiveness: reducing COBias (61% relative reduction), increasing overall accuracy (18% relative increase), and achieving robust within-task generalization across diverse prompt configurations.