Likelihood-based Mitigation of Evaluation Bias in Large Language Models

Abstract

Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics．However, the likelihood, a measure of LLM’s plausibility for a sentence, can vary due to superficial differences in sentences, such as word order and sentence structure．It is therefore possible that there might be a likelihood bias if LLMs are used for evaluation: they might overrate sentences with higher likelihoods while underrating those with lower likelihoods．In this paper, we investigate the presence and impact of likelihood bias in LLM-based evaluators．We also propose a method to mitigate the likelihood bias．Our method utilizes highly biased instances as few-shot examples for in-context learning．Our experiments in evaluating the data-to-text and grammatical error correction tasks reveal that several LLMs we test display a likelihood bias．Furthermore, our proposed method successfully mitigates this bias, also improving evaluation performance (in terms of correlation of models with human scores) significantly．