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Open Access

REVIEW

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

Ebtesam Ahmad Alomari*
Faculty of Computing and Information, Albaha University, Albaha, 65731, Saudi Arabia
* Corresponding Author: Ebtesam Ahmad Alomari. Email: email
(This article belongs to the Special Issue: Emerging Artificial Intelligence Technologies and Applications)

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.052256

Received 27 March 2024; Accepted 11 June 2024; Published online 02 July 2024

Abstract

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 reduces manual 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 ChatGPT needs to be tested using more extensive datasets and diverse data 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.

Keywords

Generative AI; large language model (LLM); natural language processing (NLP); ChatGPT; GPT (generative pre-training transformer); GPT-4; sentiment analysis; NER; information extraction; annotation; text classification

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