Unlocking the Potential:A Comprehensive Systematic Review of ChatGPT in Natural Language Processing Tasks  

作　　者：Ebtesam Ahmad Alomari 

机构地区：[1]Faculty of Computing and Information,Albaha University,Albaha,65731,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2024年第10期43-85,共43页工程与科学中的计算机建模（英文）

摘　　要：As Natural Language Processing(NLP)continues to advance,driven by the emergence of sophisticated large language models such as ChatGPT,there has been a notable growth in research activity.This rapid uptake reflects increasing interest in the field and induces critical inquiries into ChatGPT’s applicability in the NLP domain.This review paper systematically investigates the role of ChatGPT in diverse NLP tasks,including information extraction,Name Entity Recognition(NER),event extraction,relation extraction,Part of Speech(PoS)tagging,text classification,sentiment analysis,emotion recognition and text annotation.The novelty of this work lies in its comprehensive analysis of the existing literature,addressing a critical gap in understanding ChatGPT’s adaptability,limitations,and optimal application.In this paper,we employed a systematic stepwise approach following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)framework to direct our search process and seek relevant studies.Our review reveals ChatGPT’s significant potential in enhancing various NLP tasks.Its adaptability in information extraction tasks,sentiment analysis,and text classification showcases its ability to comprehend diverse contexts and extract meaningful details.Additionally,ChatGPT’s flexibility in annotation tasks reducesmanual efforts and accelerates the annotation process,making it a valuable asset in NLP development and research.Furthermore,GPT-4 and prompt engineering emerge as a complementary mechanism,empowering users to guide the model and enhance overall accuracy.Despite its promising potential,challenges persist.The performance of ChatGP Tneeds tobe testedusingmore extensivedatasets anddiversedata structures.Subsequently,its limitations in handling domain-specific language and the need for fine-tuning in specific applications highlight the importance of further investigations to address these issues.

关 键 词：Generative AI large languagemodel(LLM) natural language processing(NLP) ChatGPT GPT(generative pretraining transformer) GPT-4 sentiment analysis NER information extraction ANNOTATION text classification 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]