Summary of W-rag: Weakly Supervised Dense Retrieval in Rag For Open-domain Question Answering, by Jinming Nian et al.
W-RAG: Weakly Supervised Dense Retrieval in RAG for Open-domain Question Answering
by Jinming Nian, Zhiyuan Peng, Qifan Wang, Yi Fang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 This paper proposes a novel approach, W-RAG, to improve the performance of open-domain question answering (OpenQA) systems by utilizing Large Language Models (LLMs). The key innovation is the use of LLMs’ ranking capabilities to create weakly labeled data for training dense retrievers. Specifically, W-RAG reranks top-K passages retrieved via BM25 based on the probability that LLMs will generate the correct answer. Our comprehensive experiments across four publicly available OpenQA datasets demonstrate that W-RAG enhances both retrieval and OpenQA performance compared to baseline models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps computers better understand questions and find answers from lots of text data. Right now, these computers rely too much on what they already know instead of searching for new information. The authors developed a new way called W-RAG that uses special computer language skills to help the computers find relevant answers more effectively. They tested this method on four big datasets and found it improved how well the computers could answer questions and find important information. |
Keywords
» Artificial intelligence » Probability » Question answering » Rag
