  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.
