Prompt Perturbation Consistency Learning for Robust Language Models

arXiv:2402.15833v1 Announce Type: cross
Abstract: Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However, their performance on sequence labeling tasks such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models. Furthermore, there is a lack of substantive research on the robustness of LLMs to various perturbations in the input prompts. The contributions of …

abstract arxiv assistant classification cs.cl cs.lg labeling language language models language processing large language large language models llms natural natural language natural language processing performance personal assistant processing prompt question question answering robust summarization systems tasks text text summarization type