@inproceedings{chen-etal-2024-humans,
title = "Humans or {LLM}s as the Judge? A Study on Judgement Bias",
author = "Chen, Guiming Hardy and
Chen, Shunian and
Liu, Ziche and
Jiang, Feng and
Wang, Benyou",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.474",
doi = "10.18653/v1/2024.emnlp-main.474",
pages = "8301--8327",
abstract = "Adopting human and large language models (LLM) as judges (*a.k.a* human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this approach concurrently introduces potential biases from human and LLMs, questioning the reliability of the evaluation results. In this paper, we propose a novel framework that is free from referencing groundtruth annotations for investigating **Misinformation Oversight Bias**, **Gender Bias**, **Authority Bias** and **Beauty Bias** on LLM and human judges. We curate a dataset referring to the revised Bloom{'}s Taxonomy and conduct thousands of evaluations. Results show that human and LLM judges are vulnerable to perturbations to various degrees, and that even the cutting-edge judges possess considerable biases. We further exploit these biases to conduct attacks on LLM judges. We hope that our work can notify the community of the bias and vulnerability of human- and LLM-as-a-judge, as well as the urgency of developing robust evaluation systems.",
}
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<abstract>Adopting human and large language models (LLM) as judges (*a.k.a* human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this approach concurrently introduces potential biases from human and LLMs, questioning the reliability of the evaluation results. In this paper, we propose a novel framework that is free from referencing groundtruth annotations for investigating **Misinformation Oversight Bias**, **Gender Bias**, **Authority Bias** and **Beauty Bias** on LLM and human judges. We curate a dataset referring to the revised Bloom’s Taxonomy and conduct thousands of evaluations. Results show that human and LLM judges are vulnerable to perturbations to various degrees, and that even the cutting-edge judges possess considerable biases. We further exploit these biases to conduct attacks on LLM judges. We hope that our work can notify the community of the bias and vulnerability of human- and LLM-as-a-judge, as well as the urgency of developing robust evaluation systems.</abstract>
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%0 Conference Proceedings
%T Humans or LLMs as the Judge? A Study on Judgement Bias
%A Chen, Guiming Hardy
%A Chen, Shunian
%A Liu, Ziche
%A Jiang, Feng
%A Wang, Benyou
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F chen-etal-2024-humans
%X Adopting human and large language models (LLM) as judges (*a.k.a* human- and LLM-as-a-judge) for evaluating the performance of LLMs has recently gained attention. Nonetheless, this approach concurrently introduces potential biases from human and LLMs, questioning the reliability of the evaluation results. In this paper, we propose a novel framework that is free from referencing groundtruth annotations for investigating **Misinformation Oversight Bias**, **Gender Bias**, **Authority Bias** and **Beauty Bias** on LLM and human judges. We curate a dataset referring to the revised Bloom’s Taxonomy and conduct thousands of evaluations. Results show that human and LLM judges are vulnerable to perturbations to various degrees, and that even the cutting-edge judges possess considerable biases. We further exploit these biases to conduct attacks on LLM judges. We hope that our work can notify the community of the bias and vulnerability of human- and LLM-as-a-judge, as well as the urgency of developing robust evaluation systems.
%R 10.18653/v1/2024.emnlp-main.474
%U https://aclanthology.org/2024.emnlp-main.474
%U https://doi.org/10.18653/v1/2024.emnlp-main.474
%P 8301-8327
Markdown (Informal)
[Humans or LLMs as the Judge? A Study on Judgement Bias](https://aclanthology.org/2024.emnlp-main.474) (Chen et al., EMNLP 2024)
ACL
- Guiming Hardy Chen, Shunian Chen, Ziche Liu, Feng Jiang, and Benyou Wang. 2024. Humans or LLMs as the Judge? A Study on Judgement Bias. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8301–8327, Miami, Florida, USA. Association for Computational Linguistics.