[2603.03314] Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO

Computer Science > Computation and Language arXiv:2603.03314 (cs) [Submitted on 9 Feb 2026] Title:Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO Authors:Xin Yang, Letian Li, Abudukelimu Wuerkaixi, Xuxin Cheng, Cao Liu, Ke Zeng, Xunliang Cai, Wenyuan Jiang View a PDF of the paper titled Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPO, by Xin Yang and 7 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) have demonstrated remarkable and steadily improving performance across a wide range of tasks. However, LLM performance may be highly sensitive to prompt variations especially in scenarios with limited openness or strict output formatting requirements, indicating insufficient robustness. In real-world applications, user prompts provided to LLMs often contain imperfections, which may undermine the quality of the model's responses. To address this issue, previous work has primarily focused on preprocessing prompts, employing external tools or even LLMs to refine prompt formulations in advance. However, these approaches overlook the intrinsic robustness of LLMs, and their reliance on external components introduces additional computational overhead and uncertainty. In this work, we propose a Contrastive Learning-based Inverse Direct Preference Optimization (CoIPO) method that minimizes the discrepancy between the label-aligned logits produced by the model under a clean prompt and its noisy counterpart, and ...