Summary of Epic: Effective Prompting For Imbalanced-class Data Synthesis in Tabular Data Classification Via Large Language Models, by Jinhee Kim et al.
EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models
by Jinhee Kim, Taesung Kim, Jaegul Choo
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 A novel approach called EPIC leverages balanced, grouped data samples and consistent formatting to guide large language models (LLMs) in generating accurate synthetic tabular data. The technique optimizes performance by identifying key prompt design elements for diverse applications. Evaluations on real-world datasets demonstrate that EPIC achieves state-of-the-art machine learning classification performance, improving generation efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can create realistic data without needing actual data. This is useful for testing computer programs or simulating new situations. A team of researchers created a new way to do this called EPIC. They used groups of examples that were balanced and had the same structure. This helped the AI model learn how to generate accurate synthetic data, even if some categories had much more information than others. The results show that EPIC works well and can help improve how efficiently computers can create fake data. |
Keywords
* Artificial intelligence * Classification * Machine learning * Prompt * Synthetic data
