Summary of You Only Use Reactive Attention Slice For Long Context Retrieval, by Yun Joon Soh et al.
You Only Use Reactive Attention Slice For Long Context Retrieval
by Yun Joon Soh, Hanxian Huang, Yuandong Tian, Jishen Zhao
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 Large Language Models (LLMs) are being supported with longer context windows to improve their performance. While training models for longer contexts is computationally expensive, researchers have developed alternatives like Retrieval Augmented Generation (RAG). However, most existing RAG methods use embedding-based retrieval, which struggles with long contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Supporting Large Language Models with longer context windows is a way to make them better. Right now, training models for longer contexts uses up too many computer resources. To solve this problem, people have come up with alternatives like Retrieval Augmented Generation (RAG). But most of these RAG methods don’t work well on long texts. |
Keywords
» Artificial intelligence » Embedding » Rag » Retrieval augmented generation
