Summary of Tabular Transfer Learning Via Prompting Llms, by Jaehyun Nam et al.
Tabular Transfer Learning via Prompting LLMs
by Jaehyun Nam, Woomin Song, Seong Hyeon Park, Jihoon Tack, Sukmin Yun, Jaehyung Kim, Kyu Hwan Oh, Jinwoo Shin
First submitted to arxiv on: 9 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 framework for tabular transfer learning, Prompt to Transfer (P2T), is proposed to tackle the scarcity of labeled data in real-world machine learning applications. By leveraging large language models (LLMs) and utilizing unlabeled source data with P2T, the framework creates pseudo-demonstrations for prompts to facilitate knowledge transfer from multiple sources. The approach demonstrates promising results on various tabular learning benchmarks, outperforming previous methods. This work showcases potential solutions for the underexplored tabular transfer learning problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Tabular transfer learning is a challenging problem that arises when we have limited labeled data in real-world machine learning applications. To solve this issue, researchers are exploring ways to use knowledge from multiple sources. A new approach called Prompt to Transfer (P2T) uses large language models and takes advantage of unlabeled source data to create pseudo-examples for prompts. This method has shown great results on various benchmark tests. |
Keywords
» Artificial intelligence » Machine learning » Prompt » Transfer learning
