Summary of A Survey on Data Selection For Language Models, by Alon Albalak et al.
A Survey on Data Selection for Language Models
by Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 proposed comprehensive review aims to bridge the knowledge gap in data selection methods for large language models. By analyzing existing literature, a taxonomy of approaches is presented, highlighting effective practices and areas for improvement. The study’s findings are expected to accelerate progress in this crucial aspect of model development, making it more accessible to researchers. The authors’ work draws attention to noticeable holes in the current research landscape, providing promising avenues for future investigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have achieved great success thanks to massive text datasets used for unsupervised pre-training. However, not all data is created equal, and selecting the right data points can be crucial. By reviewing existing methods, researchers hope to improve their understanding of which data to use and how to choose it wisely. This review aims to help close the gap in knowledge by providing a clear overview of what’s currently known and where future research should focus. |
Keywords
* Artificial intelligence * Attention * Unsupervised
