UDA: Unsupervised Debiasing Alignment for Pair-wise LLM-as-a-Judge

TL;DR

This paper introduces UDA, an unsupervised framework that reduces bias in pairwise LLM evaluations by aligning judges' ratings towards a collective consensus, improving consistency and correlation with human judgments.

Contribution

UDA is a novel unsupervised method that dynamically adjusts judge ratings to minimize disagreement, enhancing evaluation stability without requiring labeled data.

Findings

Reduces inter-judge rating standard deviation by up to 63.4%.

Improves average correlation with human judgments by 24.7%.

Elevates performance of weaker judges to match stronger ones.

Abstract

Pairwise evaluation of Large Language Models (LLMs) is a common paradigm, but it is prone to preference bias, where judges systematically favor certain outputs, such as their own. This bias leads to inconsistent and skewed rankings across different judges. To address this, we first empirically demonstrate significant and heterogeneous biases in cross-model evaluations. We then propose UDA (Unsupervised Debiasing Alignment), a framework that reduces inter-judge disagreement by dynamically adjusting the Elo rating system. For each pairwise comparison, a compact neural network learns to adaptively set the K-factor and refine win probabilities. Crucially, UDA operates in a fully unsupervised manner, guided solely by the objective of minimizing the dispersion among the Elo trajectories of all judges. This forces an alignment towards a collective consensus, which serves as an unsupervised proxy for a more stable and reproducible evaluation. In addition, we provide theoretical motivation demonstrating how alignment towards a consensus can reduce aggregate system bias. Experiments show that UDA significantly reduces the inter-judge rating standard deviation by up to 63.4% and improves the average correlation with human judgments by 24.7%. Notably, UDA elevates the performance of poorly performing judges to achieve parity with high-quality ones, fostering a more robust and reliable evaluation ecosystem. Code and data are available at https://anonymous.4open.science/r/62AB93CD-23B4.