Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets
This work addresses trust issues in language models for users relying on stable outputs, but it is incremental as it builds on existing fine-tuning and prompting techniques.
The study tackled the problem of language models being sensitive to input perturbations by introducing a methodology to evaluate and enhance robustness across various model scales, using fine-tuning and chain-of-thought prompting to maintain accuracy on the original dataset while addressing multiple perturbations.
Language models, characterized by their black-box nature, often hallucinate and display sensitivity to input perturbations, causing concerns about trust. To enhance trust, it is imperative to gain a comprehensive understanding of the model's failure modes and develop effective strategies to improve their performance. In this study, we introduce a methodology designed to examine how input perturbations affect language models across various scales, including pre-trained models and large language models (LLMs). Utilizing fine-tuning, we enhance the model's robustness to input perturbations. Additionally, we investigate whether exposure to one perturbation enhances or diminishes the model's performance with respect to other perturbations. To address robustness against multiple perturbations, we present three distinct fine-tuning strategies. Furthermore, we broaden the scope of our methodology to encompass large language models (LLMs) by leveraging a chain of thought (CoT) prompting approach augmented with exemplars. We employ the Tabular-NLI task to showcase how our proposed strategies adeptly train a robust model, enabling it to address diverse perturbations while maintaining accuracy on the original dataset. https://msin-infotabs.github.io/