Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study
<|reference_start|>Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study: Utilizing Large Language Models (LLMs) as evaluators for evaluating the performance of LLMs has recently garnered attention. However, this kind of evaluation approach is affected by potential biases in LLMs, raising concerns about the accuracy and reliability of the evaluation results. To mitigate this issue, we propose and study two many-shot ICL prompts, which rely on two versions of many-shot ICL prompt templates for helping LLM evaluators to mitigate the potential biases in LLMs, \textbf{M}any-\textbf{S}hot \textbf{w}ith \textbf{R}eference (\textbf{MSwR}) and \textbf{M}any-\textbf{S}hot with\textbf{o}ut \textbf{R}eference (\textbf{MSoR}). Concretely, the former utilizes in-context examples with model-generated rationales as guidance, and the latter without. Based on the designed prompts, we investigate the impact of scaling the number of in-context examples on the consistency and quality of the evaluation results. Experimental results show that advanced LLMs, such as GPT-4o, perform better in the many-shot regime than in the zero-shot regime. Furthermore, we reveal the symbol bias hidden in the selection bias of LLMs and propose a simple yet effective approach to mitigate the bias. Experimental results further verify the effectiveness of the symbol bias mitigation approach.<|reference_end|>
@article{song2024can,
    title={Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary
  Empirical Study},
    author={Mingyang Song, Mao Zheng, Xuan Luo},
    journal={arXiv preprint arXiv:2406.11629},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11629},
    primaryClass={cs.CL}
}