Likelihood-based Mitigation of Evaluation Bias in Large Language Models
<|reference_start|>Likelihood-based Mitigation of Evaluation Bias in Large Language Models: 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.<|reference_end|>
@article{ohi2024likelihood-based,
    title={Likelihood-based Mitigation of Evaluation Bias in Large Language Models},
    author={Masanari Ohi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, Naoaki
  Okazaki},
    journal={ACL2024 (findings)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15987},
    primaryClass={cs.CL cs.AI}
}