Summary of Longrag: Enhancing Retrieval-augmented Generation with Long-context Llms, by Ziyan Jiang et al.
LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMs
by Ziyan Jiang, Xueguang Ma, Wenhu Chen
First submitted to arxiv on: 21 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 The proposed LongRAG framework addresses the limitations of traditional retrievers by processing entire Wikipedia corpus into large units, reducing the number of retrieval units and alleviating the imbalance between heavy retriever and light reader. This approach achieves strong retrieval performance on Wikipedia-based datasets NQ and HotpotQA, matching state-of-the-art (SoTA) models without requiring full training. The framework also demonstrates promising results on non-Wikipedia-based datasets Qasper and MultiFieldQA-en by processing individual documents as single units. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The LongRAG framework is a new way of doing natural language processing that can help machines understand human questions better. It works by breaking down large amounts of text into smaller chunks, called units, which makes it easier for computers to find the right answers. This approach has been shown to be very effective on certain types of datasets and is an important step towards making question-answering systems more powerful. |
Keywords
» Artificial intelligence » Natural language processing » Question answering
