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

Multi-Set Inoculation: Assessing Model Robustness Across Multiple Challenge Sets