Robustness of Large Language Models to Perturbations in Text

Understand

Having a clean dataset has been the foundational assumption of most natural language processing (NLP) systems.

• However, properly written text is rarely found in real-world scenarios and hence, oftentimes invalidates the aforementioned foundational assumption.
• Recently, Large language models (LLMs) have shown impressive performance, but can they handle the inevitable noise in real-world data? This work tackles this critical question by investigating LLMs' resilience against morphological variations in text.
• To that end, we artificially introduce varying levels of noise into a diverse set of datasets and systematically evaluate LLMs' robustness against the corrupt variations of the original text.