Bias in the Mirror : Are LLMs opinions robust to their own adversarial attacks
Virgile Rennard, Christos Xypolopoulos, Michalis Vazirgiannis
Abstract
Large language models (LLMs) inherit biases from their training data and alignment processes, influencing their responses in subtle ways. While many studies have examined these biases, little work has explored their robustness during interactions. In this paper, we introduce a novel approach where two instances of an LLM engage in self-debate, arguing opposing viewpoints to persuade a neutral version of the model. Through this, we evaluate how firmly biases hold and whether models are susceptible to reinforcing misinformation or shifting to harmful viewpoints. Our experiments span multiple LLMs of varying sizes, origins, and languages, providing deeper insights into bias persistence and flexibility across linguistic and cultural contexts.
BibTeX
@inproceedings{rennard-etal-2025-bias,
title = "Bias in the Mirror : Are {LLM}s opinions robust to their own adversarial attacks",
author = "Rennard, Virgile and
Xypolopoulos, Christos and
Vazirgiannis, Michalis",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.106/",
doi = "10.18653/v1/2025.acl-long.106",
pages = "2128--2143",
ISBN = "979-8-89176-251-0"
}