Summary of Efficientrag: Efficient Retriever For Multi-hop Question Answering, by Ziyuan Zhuang et al.
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering
by Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
First submitted to arxiv on: 8 Aug 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 A novel approach to retrieval-augmented generation (RAG) is proposed, tackling complex multi-hop queries with improved efficiency. The method, called EfficientRAG, iteratively generates new queries without requiring large language model (LLM) calls at each iteration and filters out irrelevant information. This efficient retriever surpasses existing RAG methods on three open-domain multi-hop question-answering datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers created a better way to answer complex questions by asking multiple smaller questions. They called this new method EfficientRAG. It’s faster because it doesn’t need to ask a huge language model for help each time. Instead, it asks smaller questions and filters out the unimportant answers. This made it work better on three big datasets of questions. |
Keywords
* Artificial intelligence * Language model * Large language model * Question answering * Rag * Retrieval augmented generation
