Summary of Retrieval-augmented Data Augmentation For Low-resource Domain Tasks, by Minju Seo et al.
Retrieval-Augmented Data Augmentation for Low-Resource Domain Tasks
by Minju Seo, Jinheon Baek, James Thorne, Sung Ju Hwang
First submitted to arxiv on: 21 Feb 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 The researchers tackle a long-standing issue in language modeling by developing a novel approach to augmenting training data in low-resource settings. They propose Retrieval-Augmented Data Augmentation (RADA), which retrieves relevant examples from other datasets and uses them to generate new samples with contextual information. This method ensures generated data is both relevant and diverse, outperforming existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper solves a problem in language models that perform poorly when given limited training data. They came up with a clever way to use examples from other datasets to help the model learn more effectively. By using this new approach, they were able to make their language model work better even when it had very little information to start with. |
Keywords
* Artificial intelligence * Data augmentation * Language model
