Likelihood-based Mitigation of Evaluation Bias in Large Language Models

• URL: http://arxiv.org/abs/2402.15987v3
• Date: Sat, 12 Oct 2024 09:57:43 GMT
• Title: Likelihood-based Mitigation of Evaluation Bias in Large Language Models
• Authors: Masanari Ohi, Masahiro Kaneko, Ryuto Koike, Mengsay Loem, Naoaki Okazaki,
• Abstract summary: Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. It is possible that there might be a likelihood bias if LLMs are used for evaluation.
• Score: 37.07596663793111
• License:
• 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.

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