Summary of Refiner: Restructure Retrieval Content Efficiently to Advance Question-answering Capabilities, by Zhonghao Li et al.
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
by Zhonghao Li, Xuming Hu, Aiwei Liu, Kening Zheng, Sirui Huang, Hui Xiong
First submitted to arxiv on: 17 Jun 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 new approach to improve the performance of Large Language Models (LLMs) in knowledge-extensive tasks is proposed. LLMs are limited by their parametric knowledge, leading to hallucinations and reduced accuracy. To address this, Retrieval-Augmented Generation (RAG) incorporates external document chunks, but still struggles with scattered key information. The proposed Refiner paradigm uses a single decoder-only LLM to adaptively extract query-relevant contents, restructuring the content for better LLM recognition. Experiments show significant gains in answer accuracy and outperforms state-of-the-art approaches in single-hop and multi-hop QA tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate text. But they have a problem: they get stuck on certain facts and can’t figure out the rest. To fix this, scientists created a new way to use external information to help LLMs learn more. This approach is called Retrieval-Augmented Generation (RAG). However, even with RAG, LLMs still struggle to find important details that are scattered throughout the text. That’s why the Refiner paradigm was developed: it helps LLMs reorganize the information they find so they can better understand and use it. |
Keywords
» Artificial intelligence » Decoder » Rag » Retrieval augmented generation
