Mitigating Scoring Bias in LLM-as-a-Judge via Random Number Generation

2026-08-06

AI Summary

This work addresses context-independent scoring biases exhibited by large language models when serving as evaluators. It proposes a novel prompting-based approach that guides the model to generate random numbers, enabling quantification of task-specific numerical bias by measuring the deviation of its output distribution from uniformity. Leveraging this bias estimate, the method adaptively corrects token-generation probabilities during scoring, achieving task-aware debiasing. Evaluated across four benchmark tasks, the proposed technique significantly outperforms both uncorrected models and existing calibration methods, demonstrating that scoring bias is inherently dependent on the model, the specific task, and the score range. The results confirm substantial improvements in scoring consistency and reliability.

Abstract

Large Language Models (LLMs) are often used as evaluators of text quality, known as LLM-as-a-Judge, which can outperform conventional automatic evaluation metrics that rely on reference texts. However, LLM evaluators tend to generate particular scores regardless of the context of the evaluated text, which is known as scoring bias. This study proposes a novel method to mitigate this scoring bias. An LLM is instructed to randomly generate number tokens, and the latent numerical bias of the LLM is identified by measuring the deviation of the observed distribution of numbers from the uniform distribution. A definition of a downstream task, for which an LLM evaluator is used, is added to the prompts for random number generation to measure task-specific latent number bias. In the evaluation by an LLM, the token generation probabilities for a given input are rectified considering the LLM's latent number bias. Results of the experiment on four different tasks, evaluation of LLM alignment, evaluation of summarization, Semantic Textual Similarity, and Semantic Textual Relatedness, demonstrate that our proposed method outperforms the baselines, including an LLM without debiasing and previous calibration methods. In addition, it is confirmed that scoring bias varies across LLMs, tasks, and score ranges, indicating the importance of measuring latent number bias as the case may be.

Problem

• scoring bias
• LLM-as-a-Judge
• large language models
• evaluation bias
• numerical bias

Innovation

• scoring bias
• LLM-as-a-Judge
• random number generation
• bias calibration
• latent numerical bias