Summary of Optimised Grouped-query Attention Mechanism For Transformers, by Yuang Chen et al.
Optimised Grouped-Query Attention Mechanism for Transformers
by Yuang Chen, Cheng Zhang, Xitong Gao, Robert D. Mullins, George A. Constantinides, Yiren Zhao
First submitted to arxiv on: 21 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 The proposed AsymGQA method transforms a multi-head attention (MHA) into a grouped-query attention (GQA) by asymmetrically grouping neighbour queries, allowing for better model performance within the same model size budget. This approach outperforms traditional GQA methods in large language models (LLMs), such as LLaMA-2-7B, achieving an accuracy increase of 7.5% on MMLU compared to neighbour grouping. By addressing the trade-off problem between model performance and hardware efficiency, AsymGQA has the potential to improve the scalability of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AsymGQA is a new way to make large language models work better by changing how they pay attention to different parts of text. This helps the model understand what’s important and what’s not. The new method works by grouping similar ideas together, which makes it faster and more accurate. It even outperforms some other methods that do something similar! This is exciting because it could help us make these models better and more useful for things like language translation and text summarization. |
Keywords
» Artificial intelligence » Attention » Llama » Multi head attention » Summarization » Translation
