Summary of Efficiency at Scale: Investigating the Performance Of Diminutive Language Models in Clinical Tasks, by Niall Taylor et al.
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
by Niall Taylor, Upamanyu Ghose, Omid Rohanian, Mohammadmahdi Nouriborji, Andrey Kormilitzin, David Clifton, Alejo Nevado-Holgado
First submitted to arxiv on: 16 Feb 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 In this research paper, scientists explore the effectiveness of Parameter Efficient Fine-tuning (PEFT) methods in creating specialized language models for clinical decision-making tasks. The study compares various PEFT approaches on a range of model sizes, including tiny models with just 25 million parameters. The goal is to develop smaller, more efficient models that can be fine-tuned without requiring massive computational resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research investigates how we can use special techniques to make language models better for making medical decisions. They tested different ways to do this on many different-sized models, including tiny ones with only 25 million parts. This helps us create smaller, faster models that are still very good at helping doctors make the right choices. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
