Summary of Mixture Of Scales: Memory-efficient Token-adaptive Binarization For Large Language Models, by Dongwon Jo et al.
Mixture of Scales: Memory-Efficient Token-Adaptive Binarization for Large Language Models
by Dongwon Jo, Taesu Kim, Yulhwa Kim, Jae-Joon Kim
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces a novel binarization technique called Mixture of Scales (BinaryMoS) to reduce the size of large language models (LLMs). Unlike conventional methods, BinaryMoS employs multiple scaling experts for binary weights and dynamically merges these experts at the token level to generate adaptive scaling factors. This approach enhances the representational power of binarized LLMs by allowing contextual adjustments to binary weight values. The paper demonstrates that BinaryMoS outperforms traditional static binarization techniques in various natural language processing tasks, including 2-bit quantization methods, while maintaining similar model size. The authors’ experimental results show that BinaryMoS effectively addresses the issue of diminishing linguistic effectiveness in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make big language models smaller without losing their ability to understand language. Current ways of making language models smaller don’t work well because they reduce the model’s ability to understand language. The authors created a new method called BinaryMoS that adjusts the weights in the model based on the context, which makes it better at understanding language. They tested this method and found that it performs better than other methods that make language models smaller. This is important because it could help us create more efficient language models that can be used for things like chatbots or language translation. |
Keywords
» Artificial intelligence » Natural language processing » Quantization » Token » Translation
