Summary of Sparsing Law: Towards Large Language Models with Greater Activation Sparsity, by Yuqi Luo et al.
Sparsing Law: Towards Large Language Models with Greater Activation Sparsity
by Yuqi Luo, Chenyang Song, Xu Han, Yingfa Chen, Chaojun Xiao, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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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 presents a comprehensive study on the correlation between activation sparsity and influential factors in decoder-only Transformer-based large language models (LLMs). The authors propose PPL-p% sparsity, a precise and performance-aware activation sparsity metric. They find that different activation functions exhibit comparable performance but opposite training-time sparsity trends. Additionally, they discover power-law relationships between the amount of training data and the activation ratio for SiLU-activated and ReLU-activated LLMs. The study also shows that the limit value of activation sparsity varies weakly with the parameter scale. These findings have important implications for making LLMs more efficient and interpretable. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make language models better by getting rid of unnecessary parts. It finds that different ways of doing this (called activation functions) work equally well, but some are faster than others. The study also shows that the amount of data used to train the model affects its performance in a predictable way. Finally, it discovers that the quality of the model’s internal workings doesn’t change much as you increase or decrease the total number of calculations it does. |
Keywords
» Artificial intelligence » Decoder » Relu » Transformer
