Summary of Data Generation Using Large Language Models For Text Classification: An Empirical Case Study, by Yinheng Li et al.
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
by Yinheng Li, Rogerio Bonatti, Sara Abdali, Justin Wagle, Kazuhito Koishida
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers investigate the effectiveness of using Large Language Models (LLMs) to generate synthetic training data for text classification tasks. They explore how various factors, such as prompt choice, task complexity, and generated data quality, quantity, and diversity, affect the outcome. The authors use natural language understanding models trained on synthetic data to evaluate the quality of different generation approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machines to create fake training data for text classification tasks. Researchers want to know how good this fake data is and why some methods work better than others. They used special AI models that can understand language to see which method creates the best fake data. |
Keywords
» Artificial intelligence » Language understanding » Prompt » Synthetic data » Text classification
