Summary of Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other, by Yifei Gao et al.
Compensate Quantization Errors: Make Weights Hierarchical to Compensate Each Other
by Yifei Gao, Jie Ou, Lei Wang, Yuting Xiao, Zhiyuan Xiang, Ruiting Dai, Jun Cheng
First submitted to arxiv on: 24 Jun 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 This paper proposes an advanced solution called Learnable Singular value Increment (LSI) to address the accuracy decay caused by quantization in Emergent Large Language Models. The proposed method combines two existing approaches: one that uses other weights to compensate for quantization error and another that transfers the quantization difficulty to other parts of the model. LSI uses Singular Value Decomposition to extract singular values of the weights, making them learnable to help weights compensate each other conditioned on activation. The authors demonstrate state-of-the-art performance in various quantization settings, including weight-only, weight-activation, and extremely low-bit scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make language models work better after they’ve been reduced to use less computer memory and energy. Traditional methods for doing this cause the model’s accuracy to decrease, but this new method, called Learnable Singular value Increment (LSI), tries to fix that problem. It works by using a special technique called Singular Value Decomposition to help the model’s weights work together better. The authors tested their method on different types of language models and found it worked really well. |
Keywords
» Artificial intelligence » Quantization
