Language models, given their black-box nature, often exhibit sensitivity to input perturbations, leading to trust issues due to hallucinations. To bolster trust, it's essential to understand these models' failure modes and devise strategies to enhance their performance. In this study, we propose a framework to study the effect of input perturbations on language models of different scales, from pre-trained models to large language models (LLMs). We use fine-tuning to train a robust model to perturbations, and we investigate whether exposure to one perturbation improves or degrades the model's performance on other perturbations. To address multi-perturbation robustness, we suggest three distinct training strategies. We also extend the framework to LLMs via a chain of thought(COT) prompting with exemplars. We instantiate our framework for the Tabular-NLI task and show that the proposed strategies train the model robust to different perturbations without losing accuracy on a given dataset.

翻译：语言模型由于其黑箱特性，往往对输入扰动表现出敏感性，导致因幻觉现象而产生信任问题。为增强信任，理解这些模型的失效模式并制定提升其性能的策略至关重要。在本研究中，我们提出一个框架，以研究不同规模语言模型（从预训练模型到大型语言模型）在输入扰动下的影响。我们通过微调训练一个对扰动具有鲁棒性的模型，并探究暴露于一种扰动是否会提升或降低模型在其他扰动上的性能。针对多扰动鲁棒性问题，我们提出三种不同的训练策略。此外，我们通过带有示例的思维链提示将框架扩展至大型语言模型。我们在表格自然语言推理任务上实例化该框架，并表明所提出的策略能够训练模型对多种扰动保持鲁棒性，同时不损失在给定数据集上的准确性。