  With the increasing capabilities of large language models (LLMs), these
high-performance models have achieved state-of-the-art results on a wide range
of natural language processing (NLP) tasks. However, the models' performance on
commonly-used benchmark datasets often fails to accurately reflect their
reliability and robustness when applied to real-world noisy data. To address
these challenges, we propose a unified robustness evaluation framework based on
the slot-filling task to systematically evaluate the dialogue understanding
capability of LLMs in diverse input perturbation scenarios. Specifically, we
construct a input perturbation evaluation dataset, Noise-LLM, which contains
five types of single perturbation and four types of mixed perturbation data.
Furthermore, we utilize a multi-level data augmentation method (character,
word, and sentence levels) to construct a candidate data pool, and carefully
design two ways of automatic task demonstration construction strategies
(instance-level and entity-level) with various prompt templates. Our aim is to
assess how well various robustness methods of LLMs perform in real-world noisy
scenarios. The experiments have demonstrated that the current open-source LLMs
generally achieve limited perturbation robustness performance. Based on these
experimental observations, we make some forward-looking suggestions to fuel the
research in this direction.
