Summary of Scaling Efficient Llms, by B.n. Kausik
Scaling Efficient LLMs
by B.N. Kausik
First submitted to arxiv on: 22 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 This paper investigates efficient language models (LLMs), which are typically sparse with most parameters set to zero. To achieve desired accuracy on a training corpus while minimizing parameters, we compare theoretical and empirical estimates for training loss. Our findings imply that to double the number of skills in a training corpus, it must scale more than fourfold. We also discover that efficient LLMs require a scaling relationship between the number of parameters (N) and the size (D) of a natural training corpus, where N is proportional to D^0.44. Additionally, our results suggest that scaling up can uncover emergent skills when the number of parameters is smaller than the number of unique sequences in the training corpus. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make language models more efficient while still being accurate. It compares different ideas for measuring how well a model is doing and finds some surprising relationships between how big the model is and how many skills it can learn from a certain amount of data. The main finding is that if you want to double the number of things a model can do, you need at least four times more training data. This has implications for building better language models in the future. |
