Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation
GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processe...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/15/2/99 |
| _version_ | 1797297941735538688 |
|---|---|
| author | Fahim Sufi |
| author_facet | Fahim Sufi |
| author_sort | Fahim Sufi |
| collection | DOAJ |
| description | GPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits. |
| first_indexed | 2024-03-07T22:27:38Z |
| format | Article |
| id | doaj.art-65c6d3fe900b4483a8f36e64b5ed4d9a |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-07T22:27:38Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-65c6d3fe900b4483a8f36e64b5ed4d9a2024-02-23T15:21:07ZengMDPI AGInformation2078-24892024-02-011529910.3390/info15020099Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data AugmentationFahim Sufi0School of Public Health and Preventive Medicine, Monash University, 553 St. Kilda Rd., Melbourne, VIC 3004, AustraliaGPT (Generative Pre-trained Transformer) represents advanced language models that have significantly reshaped the academic writing landscape. These sophisticated language models offer invaluable support throughout all phases of research work, facilitating idea generation, enhancing drafting processes, and overcoming challenges like writer’s block. Their capabilities extend beyond conventional applications, contributing to critical analysis, data augmentation, and research design, thereby elevating the efficiency and quality of scholarly endeavors. Strategically narrowing its focus, this review explores alternative dimensions of GPT and LLM applications, specifically data augmentation and the generation of synthetic data for research. Employing a meticulous examination of 412 scholarly works, it distills a selection of 77 contributions addressing three critical research questions: (1) GPT on Generating Research data, (2) GPT on Data Analysis, and (3) GPT on Research Design. The systematic literature review adeptly highlights the central focus on data augmentation, encapsulating 48 pertinent scholarly contributions, and extends to the proactive role of GPT in critical analysis of research data and shaping research design. Pioneering a comprehensive classification framework for “GPT’s use on Research Data”, the study classifies existing literature into six categories and 14 sub-categories, providing profound insights into the multifaceted applications of GPT in research data. This study meticulously compares 54 pieces of literature, evaluating research domains, methodologies, and advantages and disadvantages, providing scholars with profound insights crucial for the seamless integration of GPT across diverse phases of their scholarly pursuits.https://www.mdpi.com/2078-2489/15/2/99LLMGPTsystematic literature reviewGPT in researchdata augmentationfeature extraction |
| spellingShingle | Fahim Sufi Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation Information LLM GPT systematic literature review GPT in research data augmentation feature extraction |
| title | Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation |
| title_full | Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation |
| title_fullStr | Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation |
| title_full_unstemmed | Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation |
| title_short | Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation |
| title_sort | generative pre trained transformer gpt in research a systematic review on data augmentation |
| topic | LLM GPT systematic literature review GPT in research data augmentation feature extraction |
| url | https://www.mdpi.com/2078-2489/15/2/99 |
| work_keys_str_mv | AT fahimsufi generativepretrainedtransformergptinresearchasystematicreviewondataaugmentation |