Summary of Mix-of-granularity: Optimize the Chunking Granularity For Retrieval-augmented Generation, by Zijie Zhong et al.
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation
by Zijie Zhong, Hanwen Liu, Xiaoya Cui, Xiaofan Zhang, Zengchang Qin
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This research proposes Mix-of-Granularity (MoG), a method that dynamically determines the optimal granularity of knowledge sources for Retrieval-Augmented Generation (RAG) systems. Inspired by Mix-of-Expert, MoG uses a router to efficiently train a newly proposed loss function with soft labels. The authors extend MoG to MoG-Graph (MoGG), which pre-processes reference documents as graphs for retrieving distantly situated snippets. Experiments demonstrate that MoG and MoGG enhance the performance of RAG systems in downstream tasks, such as predicting optimal granularity levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Retrieval-Augmented Generation (RAG) is a way to generate text based on information from multiple sources. The problem is that each source has its own rules and structure, making it hard for computers to find what they need. To solve this, the authors created a new method called Mix-of-Granularity (MoG). MoG helps computers decide which level of detail to look at in each source based on the question being asked. This makes the computer search more effective. The authors also added a new way to process information from multiple sources called MoG-Graph, which allows computers to find specific parts of text even if they’re far apart. |
Keywords
» Artificial intelligence » Loss function » Rag » Retrieval augmented generation
