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2023 EMNLP EMNLP 2023

Is ChatGPT the ultimate Data Augmentation Algorithm?

Abstract

AbstractIn the aftermath of GPT-3.5, commonly known as ChatGPT, research have attempted to assess its capacity for lowering annotation cost, either by doing zero-shot learning, generating new data, or replacing human annotators. Some studies have also investigated its use for data augmentation (DA), but only in limited contexts, which still leaves the question of how ChatGPT performs compared to state-of-the-art algorithms. In this paper, we use ChatGPT to create new data both with paraphrasing and with zero-shot generation, and compare it to seven other algorithms. We show that while ChatGPT performs exceptionally well on some simpler data, it overall does not perform better than the other algorithms, yet demands a much larger implication from the practitioner due to the ChatGPT often refusing to answer due to sensitive content in the datasets.

❓ The Questioner
🌉 Interdisciplinary Bridge — Deep Learning and Machine Learning and Natural Language Processing
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Frédéric Piedboeuf , Philippe Langlais

Topics

Machine Learning > Application Areas > Data Augmentation Natural Language Processing > Resources & Methods > Large Language Models Deep Learning > Models > Large Language Models Machine Learning > Learning Paradigms > Zero-Shot Learning Deep Learning > Learning Types > Zero-Shot Learning Deep Learning > Learning Types > Data Augmentation

Keywords

zero-shot learning text classification data augmentation zero-shot generation large language model
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