Summary of Enhancing Retrieval and Managing Retrieval: a Four-module Synergy For Improved Quality and Efficiency in Rag Systems, by Yunxiao Shi et al.
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
by Yunxiao Shi, Xing Zi, Zijing Shi, Haimin Zhang, Qiang Wu, Min Xu
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 paper proposes enhancements to Retrieval-Augmented Generation (RAG) techniques, which leverage large language models (LLMs) for more accurate and relevant responses. The RAG framework has evolved from the ‘retrieve-then-read’ approach to a modular paradigm. Specifically, the Query Rewriter module is critical, as it generates search-friendly queries that align input questions with knowledge bases. To overcome Information Plateaus, the paper suggests generating multiple queries (Query Rewriter+) and rewriting questions to eliminate ambiguity. Additionally, the Knowledge Filter addresses Irrelevant Knowledge issues in current RAG systems. The Memory Knowledge Reservoir and Retriever Trigger are introduced to solve Redundant Retrieval, optimizing resource utilization and response efficiency. These four modules synergistically improve response quality and efficiency, validated through experiments on six QA datasets. The Gemma-2B model is used as the instruction-tuned basis for these modules. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how to make computers better at answering questions. It looks at a technique called Retrieval-Augmented Generation (RAG) and finds ways to improve it. RAG uses special computer models to generate answers. One way to make it better is by asking more questions to get the right answer. Another way is to rephrase the question so it’s clearer what you’re looking for. The paper also talks about getting rid of extra information that’s not helpful and finding ways to use computer resources more efficiently. By making these improvements, computers can give better answers to questions. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
