Practical Solutions and Value of Comparative Analysis of LLM and Traditional Text Augmentation
Revolutionizing Textual Dataset Augmentation
Large Language Models (LLMs) like GPT-4, Gemini, and Llama offer new possibilities for enhancing small downstream classifiers.
Challenges: High computational costs, power consumption, CO2 emissions.
Research on Augmentation Techniques
Explored text augmentation techniques to enhance language model performance.
Established methods include character-based augmentations, backtranslation, and earlier language models for paraphrasing.
Comprehensive Comparative Analysis
Research compares established and LLM-based text augmentation methods through an extensive experimental design.
Investigates paraphrasing, contextual word insertion, and word swap across six diverse datasets and various classification tasks.
Results and Recommendations
LLM-based paraphrasing outperformed other LLM methods in 56% of cases.
LLM-based methods are primarily beneficial in low-resource settings with statistically significant improvements and higher relative increases in model accuracy.
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