Summary of Language Models As Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models, by Hui-po Wang et al.
Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models
by Hui-Po Wang, Mario Fritz
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper demonstrates the potential of large language models (LLMs) as gradient priors in a zero-shot setting, focusing on lossless gradient compression for distributed learning. The authors introduce LM-GC, a novel method integrating LLMs with arithmetic coding to convert plain gradients into text-like formats, achieving up to 38 times higher token efficiency compared to plain representations. The approach maintains close alignment with plain gradients and recognizable symbols by LLMs. Experiments show that LM-GC surpasses state-of-the-art lossless compression methods, improving compression rates by 10% to 17.2% across various datasets and architectures. The authors also explore compatibility with lossy compression techniques like quantization and sparsification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how large language models can help with a big problem in machine learning: compressing gradients so they take up less space when sent between different parts of a computer. The authors create a new method called LM-GC that uses these language models to make the gradients smaller, without losing any important information. They test this method and show it works better than other methods at compressing gradients. This could help with big AI projects that need to work together lots of computers. |
Keywords
» Artificial intelligence » Alignment » Machine learning » Quantization » Token » Zero shot
