Summary of Aurora-m: Open Source Continual Pre-training For Multilingual Language and Code, by Taishi Nakamura et al.
Aurora-M: Open Source Continual Pre-training for Multilingual Language and Code
by Taishi Nakamura, Mayank Mishra, Simone Tedeschi, Yekun Chai, Jason T Stillerman, Felix Friedrich, Prateek Yadav, Tanmay Laud, Vu Minh Chien, Terry Yue Zhuo, Diganta Misra, Ben Bogin, Xuan-Son Vu, Marzena Karpinska, Arnav Varma Dantuluri, Wojciech Kusa, Tommaso Furlanello, Rio Yokota, Niklas Muennighoff, Suhas Pai, Tosin Adewumi, Veronika Laippala, Xiaozhe Yao, Adalberto Junior, Alpay Ariyak, Aleksandr Drozd, Jordan Clive, Kshitij Gupta, Liangyu Chen, Qi Sun, Ken Tsui, Noah Persaud, Nour Fahmy, Tianlong Chen, Mohit Bansal, Nicolo Monti, Tai Dang, Ziyang Luo, Tien-Tung Bui, Roberto Navigli, Virendra Mehta, Matthew Blumberg, Victor May, Huu Nguyen, Sampo Pyysalo
First submitted to arxiv on: 30 Mar 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 paper tackles the issue of making pretrained language models more accessible by addressing their high computational cost. It highlights the limitations of current efforts to democratize access to these models, such as Bloom and StarCoder, which still face challenges like limited multilingual capabilities, catastrophic forgetting during continual pretraining, and the need for training models from scratch. The authors discuss the importance of aligning AI development with safety standards and regulatory frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making language models more accessible by fixing some big problems that make them hard to use. Right now, it’s expensive and difficult to train these models, which means not everyone can participate. Some groups are trying to make things better, but they’re still facing challenges like speaking only one language or losing what they’ve learned when they try to improve. The authors want to find a way to balance making AI safe and following the rules. |
Keywords
* Artificial intelligence * Pretraining
