Summary of Block-attention For Efficient Rag, by East Sun et al.
Block-Attention for Efficient RAG
by East Sun, Yan Wang, Lan Tian
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The Block-Attention mechanism is designed to address latency and computation cost issues in Retrieval-Augmented Generation (RAG) scenarios. By dividing retrieved documents into discrete blocks, each block independently calculates key-value states except for the final block. This enables reusing KV states of seen passages during inference, significantly reducing latency and computation overhead. The implementation involves block segmentation, position re-encoding, and fine-tuning a Large Language Model (LLM) to adapt to Block-Attention. Experiments on four RAG benchmarks show that after block fine-tuning, the Block-Attention model achieves comparable performance to self-attention models or even surpasses them. Notably, Block-Attention significantly reduces time-to-first-token and floating-point operations by 98.7% and 99.8%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Block-Attention is a new way to help computers understand text better. It’s designed to make big machines work faster and more efficiently when they’re asked to generate text based on what they’ve learned from other texts. Instead of looking at all the text at once, Block-Attention breaks it down into smaller chunks, or “blocks.” This helps computers use what they already know about similar texts to generate new text, making them work much faster and using less energy. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Inference » Large language model » Rag » Retrieval augmented generation » Self attention » Token
