Summary of Towards Efficient Large Language Models For Scientific Text: a Review, by Huy Quoc to et al.
Towards Efficient Large Language Models for Scientific Text: A Review
by Huy Quoc To, Ming Liu, Guangyan Huang
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 comprehensive review is presented on large language models (LLMs) that can process complex scientific information, leveraging the increasing amount of scientific literature. These powerful models require significant computational resources, data, and training time, making them expensive to develop. To address this, researchers have proposed various methodologies to make LLMs more affordable, focusing either on model size or data quality. The paper investigates these two approaches, summarizing current advances in LLMs for accessible AI solutions in science. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are using powerful language models to understand and process complex scientific information. These models need lots of computer power, data, and time to train. Researchers have found ways to make them more affordable by changing the size of the model or the quality of the training data. This paper looks at these different approaches and how they can help make AI solutions for science more accessible. |
