Using GPT-3 as a Text Data Augmentator for a Complex Text Detector

Romero-Sandoval, Mario; Calderón-Ramírez, Saúl; Solís, Martín

doi:10.1109/bip60195.2023.10379347

Cited by 2 publications

(3 citation statements)

References 31 publications

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“…The paper discusses how synthetic tweets are generated and used to improve classification performance, especially for underrepresented classes. Romero-Sandoval et al [62] investigate using GPT-3 for text simplification in Spanish financial texts, demonstrating effective data augmentation to improve classifier performance. Rebboud et al [63] explore GPT-3's ability to generate synthetic data for event relation classification, enhancing system accuracy with prompt-based, manually validated synthetic sentences.…”

Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

“…However, with the advent of GPT, features could be extracted with a simple GPT prompt like "Categorize these data into the following categories (1) animals, (2) plants, and (3) equipment," as shown in Figure 5. • Text Simplification and Classification: Covers the application of GPT in simplifying complex texts for better understanding and classification, particularly in specialized fields like finance [10,62,64,67]. • Text Augmentation and Linguistic Feature Detection: This refers to the use of GPT for generating text that aids in the detection and analysis of specific linguistic features, such as similes or event relations.…”

Section: Natural Language Processing (Nlp) and Text Analysismentioning

confidence: 99%

“…Text Simplification and Classification: Covers the application of GPT in simplifying complex texts for better understanding and classification, particularly in specialized fields like finance [10,62,64,67].…”

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confidence: 99%

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

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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.

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Section: Existing Research On Gpt's Use In Research Datamentioning

confidence: 99%

Section: Natural Language Processing (Nlp) and Text Analysismentioning

confidence: 99%

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Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

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Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

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2024

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This research confronts the persistent challenge of data scarcity in medical machine learning by introducing a pioneering methodology that harnesses the capabilities of Generative Pre-trained Transformers (GPT). In response to the limitations posed by a dearth of labeled medical data, our approach involves the synthetic generation of comprehensive patient discharge messages, setting a new standard in the field with GPT autonomously generating 20 fields. Through a meticulous review of the existing literature, we systematically explore GPT’s aptitude for synthetic data generation and feature extraction, providing a robust foundation for subsequent phases of the research. The empirical demonstration showcases the transformative potential of our proposed solution, presenting over 70 patient discharge messages with synthetically generated fields, including severity and chances of hospital re-admission with justification. Moreover, the data had been deployed in a mobile solution where regression algorithms autonomously identified the correlated factors for ascertaining the severity of patients’ conditions. This study not only establishes a novel and comprehensive methodology but also contributes significantly to medical machine learning, presenting the most extensive patient discharge summaries reported in the literature. The results underscore the efficacy of GPT in overcoming data scarcity challenges and pave the way for future research to refine and expand the application of GPT in diverse medical contexts.

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Using GPT-3 as a Text Data Augmentator for a Complex Text Detector

Cited by 2 publications

References 31 publications

Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Generative Pre-Trained Transformer (GPT) in Research: A Systematic Review on Data Augmentation

Addressing Data Scarcity in the Medical Domain: A GPT-Based Approach for Synthetic Data Generation and Feature Extraction

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