Summary of Beyond Kv Caching: Shared Attention For Efficient Llms, by Bingli Liao and Danilo Vasconcellos Vargas
Beyond KV Caching: Shared Attention for Efficient LLMs
by Bingli Liao, Danilo Vasconcellos Vargas
First submitted to arxiv on: 13 Jul 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 The novel Shared Attention (SA) mechanism introduced in this paper aims to enhance the efficiency of large language models (LLMs) by sharing computed attention weights across multiple layers. This approach reduces both computational flops and KV cache size required during inference, making it suitable for deployment in resource-constrained environments. The authors empirically demonstrate that SA implementation results in minimal accuracy loss on standard benchmarks, maintaining robust model performance while conserving resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make language models more efficient by sharing information between different parts of the model. This helps reduce the amount of computer power and memory needed to run the model, making it easier to use in situations where there are limited resources available. The authors tested their approach and found that it only slightly affects the accuracy of the model, which is important for keeping the quality of the results high. |
Keywords
» Artificial intelligence » Attention » Inference
