Robustness of Prompting: Enhancing Robustness of Large Language Models Against Prompting Attacks

arXiv:2506.03627v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across various tasks by effectively utilizing a prompting strategy. However, they are highly sensitive to input perturbations, such as typographical errors or slight character order errors, which can significantly impair their performance. Despite advances in prompting techniques such as Chain-of-Thought and automatic prompt generation, developing a prompting strategy that explicitly mitigates the negative impact of such perturbations remains an open challenge. To bridge th

Source: arXiv cs.CL — read the full report at the original publisher.

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