Summary of Attention Is All You Need but You Don’t Need All Of It For Inference Of Large Language Models, by Georgy Tyukin et al.
Attention Is All You Need But You Don’t Need All Of It For Inference of Large Language Models
by Georgy Tyukin, Gbetondji J-S Dovonon, Jean Kaddour, Pasquale Minervini
First submitted to arxiv on: 22 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 to improving the latency of Large Language Models (LLMs) is proposed, tackling the quadratic input length complexity issue in attention layers. Researchers investigate the effect of dropping specific layers at inference time on the performance of Llama-v2 models. The findings suggest that dropping certain attention layers has a minimal impact on overall performance but yields significant speedups. For instance, removing 33% of attention layers from a 13B Llama2 model results in only a 1.8% drop in average performance over the OpenLLM benchmark. This work also explores the trade-off between layer skipping and performance, demonstrating that skipping attention layers is a promising strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super-smart computers that can understand and generate human-like text. But they’re not perfect – sometimes it takes them too long to respond. Scientists have been trying to figure out how to make them faster without sacrificing their intelligence. In this research, they tested different ways of making LLMs go faster by removing certain parts from the model’s internal machinery. They found that getting rid of some attention layers didn’t hurt performance much, but it made the computer go way faster! This is important because we’re using these models for things like chatbots and language translation. |
Keywords
» Artificial intelligence » Attention » Inference » Llama » Translation
