Summary of Ragserve: Fast Quality-aware Rag Systems with Configuration Adaptation, by Siddhant Ray et al.
RAGServe: Fast Quality-Aware RAG Systems with Configuration Adaptation
by Siddhant Ray, Rui Pan, Zhuohan Gu, Kuntai Du, Ganesh Ananthanarayanan, Ravi Netravali, Junchen Jiang
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 paper presents a novel approach to Retrieval Augmented Generation (RAG) for large language models (LLMs), which balances the tradeoff between response delay and generation quality. The proposed system, RAGServe, optimizes both query scheduling and key RAG configurations to achieve better response quality while reducing latency. Experimental results on four popular datasets demonstrate that RAGServe outperforms state-of-the-art methods in terms of latency reduction (1.64-2.54x) without compromising generation quality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG is a way for big language models to get help from the internet when answering questions. Right now, this helps them give better answers but also makes it take longer. Some people have tried to make it go faster or better, but they haven’t found the right balance between speed and quality. This paper introduces RAGServe, a new system that does both at once. It looks at how often to ask for help and what kind of help to get, so it can give good answers quickly. The results show that this new system is faster than other approaches while still giving great answers. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
