In this work, we propose and study two versions of many-shot in-context prompts, Reinforced and Unsupervised ICL, for helping GPT-4o-as-a-Judge in single answer grading. The former uses in-context examples with model-generated rationales, and the latter without. Based on the designed prompts, we investigate the impact of scaling the number of in-context examples on the agreement and quality of the evaluation. Furthermore, we first reveal the symbol bias in GPT-4o-as-a-Judge for pair-wise comparison and then propose a simple yet effective approach to mitigate it. Experimental results show that advanced long-context LLMs, such as GPT-4o, perform better in the many-shot regime than in the zero-shot regime. Meanwhile, the experimental results further verify the effectiveness of the symbol bias mitigation approach.
Please find more details of this work in the paper.
Reinforced ICL for GPT-4o-as-a-Judge in Single Answer Grading
Unsupervised ICL for GPT-4o-as-a-Judge in Single Answer Grading
If you use this benchmark, please cite this paper
@misc{song2024manyshotincontextlearninghelp,
title={Can Many-Shot In-Context Learning Help Long-Context LLM Judges? See More, Judge Better!},
author={Mingyang Song and Mao Zheng and Xuan Luo},
year={2024},
eprint={2406.11629},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.11629},
}
For any questions, feel free to create an issue, and we will try our best to solve it.
If the problem is more urgent, you can email me simultaneously (I check email almost daily).
NAME: Mingyang Song
EMAIL: nickmysong@tencent.com