Summary of Dr-rag: Applying Dynamic Document Relevance to Retrieval-augmented Generation For Question-answering, by Zijian Hei and Weiling Liu and Wenjie Ou and Juyi Qiao and Junming Jiao and Guowen Song and Ting Tian and Yi Lin
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering
by Zijian Hei, Weiling Liu, Wenjie Ou, Juyi Qiao, Junming Jiao, Guowen Song, Ting Tian, Yi Lin
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 question-answering (QA) tasks is proposed, building upon recent advancements in Large Language Models (LLMs). The Retrieval-Augmented Generation (RAG) framework expands query context by incorporating external knowledge bases, enhancing response accuracy. However, accessing LLMs multiple times for each query would be inefficient, and relying on a single query to retrieve all relevant documents is unreliable. To address this, the Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG) framework is introduced, combining parts of documents with queries to mine relevance. A compact classifier is applied to determine document contribution to answering the query. Experimental results on multi-hop QA datasets show significant improvements in answer accuracy and progress in QA systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RAG has helped large language models (LLMs) do better at question-answering tasks like answering questions you find in books or articles. But it’s not perfect because sometimes we have to ask the same LLM many times, which is slow, or we might miss some important information. To fix this, researchers came up with a new way called Dynamic-Relevant Retrieval-Augmented Generation (DR-RAG). It takes pieces of documents and combines them with questions to find what’s most important. This helps answer questions more accurately and efficiently. In fact, tests showed that DR-RAG can do even better than the original RAG at answering tricky questions! |
Keywords
» Artificial intelligence » Question answering » Rag » Retrieval augmented generation
