Summary of Accelerating Retrieval-augmented Generation, by Derrick Quinn et al.
Accelerating Retrieval-Augmented Generation
by Derrick Quinn, Mohammad Nouri, Neel Patel, John Salihu, Alireza Salemi, Sukhan Lee, Hamed Zamani, Mohammad Alian
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Information Retrieval (cs.IR)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores Retrieval-Augmented Generation (RAG), a novel approach to improve large language models’ (LLMs) accuracy by incorporating information from external knowledge sources like the web. The authors profile various RAG pipelines, shedding light on the intricate relationships between retrieval and generation phases. Experimental results show that exact retrieval schemes, though computationally expensive, can reduce inference time compared to approximate variants while maintaining similar end-to-end accuracy. This finding encourages the acceleration of exact nearest neighbor search for RAG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a way to make language models better by adding information from outside sources like the internet. The paper looks at different ways to do this and how they work together. It shows that a more accurate method takes longer, but still gives the same results in the end. This means we can make it faster without losing quality. |
Keywords
» Artificial intelligence » Inference » Nearest neighbor » Rag » Retrieval augmented generation
