Summary of Weighted Grouped Query Attention in Transformers, by Sai Sena Chinnakonduru et al.
Weighted Grouped Query Attention in Transformers
by Sai Sena Chinnakonduru, Astarag Mohapatra
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 proposed Weighted Grouped-Query Attention (WGQA) model improves upon existing Grouped-Query Attention (GQA) models by introducing learnable parameters for key and value heads in the T5 decoder attention blocks. This allows the model to take a weighted average during finetuning, achieving an average improvement of 0.53% over GQA. The performance converges to traditional Multi-head attention (MHA) with no additional overhead during inference. The paper evaluates the introduction of these parameters and subsequent finetuning, showing that it informs the model about the grouping mechanism during training, thereby enhancing performance. Additionally, the scaling laws are demonstrated by comparing results between T5-small and T5-base architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to make language models better at understanding what’s important in a sentence. It builds upon existing ideas, but adds some extra features that help it learn more effectively. The result is a model that does slightly better than before, and can do it without using up too much computer memory. The researchers tested their idea by comparing it to other models, and showed that it really makes a difference. |
Keywords
» Artificial intelligence » Attention » Decoder » Inference » Multi head attention » Scaling laws » T5
