@inproceedings{
wang2023are,
title={Are Large Language Models Really Robust to Word-Level Perturbations?},
author={Haoyu Wang and Guozheng Ma and Cong Yu and Ning Gui and Linrui Zhang and Zhiqi Huang and Suwei Ma and Yongzhe Chang and Sen Zhang and Li Shen and Xueqian Wang and Peilin Zhao and Dacheng Tao},
booktitle={Socially Responsible Language Modelling Research},
year={2023},
url={https://openreview.net/forum?id=mVhOKo62Q2}
}
The swift advancement in the scales and capabilities of Large Language Models (LLMs) positions them as promising tools for a variety of downstream tasks. In addition to the pursuit of better performance and the avoidance of violent feedback on a certain prompt, to ensure the responsibility of the LLM, much attention is drawn to the robustness of LLMs. However, existing evaluation methods mostly rely on traditional question answering datasets with predefined supervised labels, which do not align with the superior generation capabilities of contemporary LLMs. To address this issue, we propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools to evaluate the longer conversation generated from more challenging open questions by LLMs, which we refer to as the  $R$eward Model for $R$easonable  $R$obustness $Eval$uation ($TREvaL$).  Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions, a capability not entirely encompassed by individual words or letters, which may exhibit oversimplification and inherent biases. Our extensive empirical experiments demonstrate that TREvaL provides an innovative method for evaluating the robustness of LLMs. Furthermore, our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage. Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted.
Are Large Language Models Really Robust to Word-Level Perturbations?
Haoyu Wang Guozheng Ma Cong Yu Ning Gui Linrui Zhang Zhiqi Huang Suwei Ma Yongzhe Chang Sen Zhang Li Shen Xueqian Wang Peilin Zhao Dacheng Tao