Summary of Discovering the Gems in Early Layers: Accelerating Long-context Llms with 1000x Input Token Reduction, by Zhenmei Shi et al.
Discovering the Gems in Early Layers: Accelerating Long-Context LLMs with 1000x Input Token Reduction
by Zhenmei Shi, Yifei Ming, Xuan-Phi Nguyen, Yingyu Liang, Shafiq Joty
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach, GemFilter, is proposed to accelerate Large Language Model (LLM) inference and reduce GPU memory consumption. LLMs excel at handling long context inputs but this comes at a cost of increased computational resources and latency. By identifying relevant tokens in early layers, GemFilter uses these as filters to select and compress input tokens, significantly reducing the context length for subsequent processing. This method achieves a 2.4x speedup and 30% reduction in GPU memory usage compared to existing techniques like standard attention and SnapKV/H2O. GemFilter is training-free, broadly applicable across different LLMs, and provides interpretability by allowing humans to inspect the selected input sequence. Evaluation on the Needle in a Haystack task shows GemFilter significantly outperforms standard attention, while demonstrating comparable performance on the LongBench challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about making Large Language Models work faster and use less memory. Right now, these models are really good at understanding long pieces of text, but it takes a lot of computer power to do so. The researchers found that by looking at what the model is doing early on, they can speed up the process and use less memory. This new method, called GemFilter, makes the model work 2.4 times faster and uses 30% less memory than before. It’s also easy for humans to understand what the model is doing, which is important. The researchers tested it on two tasks and found that it works really well. |
Keywords
» Artificial intelligence » Attention » Context length » Inference » Large language model
