While large language models (LLMs) like ChatGPT have shown impressive capabilities in Natural Language Processing (NLP) tasks, a systematic investigation of their potential in this field remains largely unexplored. This study aims to address this gap by exploring the following questions: (1) How are LLMs currently applied to NLP tasks in the literature? (2) Have traditional NLP tasks already been solved with LLMs? (3) What is the future of the LLMs for NLP? To answer these questions, we take the first step to provide a comprehensive overview of LLMs in NLP. Specifically, we first introduce a unified taxonomy including (1) parameter-frozen paradigm and (2) parameter-tuning paradigm to offer a unified perspective for understanding the current progress of LLMs in NLP. Furthermore, we summarize the new frontiers and the corresponding challenges, aiming to inspire further groundbreaking advancements. We hope this work offers valuable insights into the potential and limitations of LLMs, while also serving as a practical guide for building effective LLMs in NLP.
@article{arxiv.2405.12819,
title = {Large Language Models Meet NLP: A Survey},
author = {Libo Qin and Qiguang Chen and Xiachong Feng and Yang Wu and Yongheng Zhang and Yinghui Li and Min Li and Wanxiang Che and Philip S. Yu},
journal= {arXiv preprint arXiv:2405.12819},
year = {2025}
}
Comments
The article has been accepted by Frontiers of Computer Science (FCS), with the DOI: {10.1007/s11704-025-50472-3}