Summary of Structrag: Boosting Knowledge Intensive Reasoning Of Llms Via Inference-time Hybrid Information Structurization, by Zhuoqun Li et al.
StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization
by Zhuoqun Li, Xuanang Chen, Haiyang Yu, Hongyu Lin, Yaojie Lu, Qiaoyu Tang, Fei Huang, Xianpei Han, Le Sun, Yongbin Li
First submitted to arxiv on: 11 Oct 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 A new framework called StructRAG is proposed to enhance large language models (LLMs) in knowledge-intensive tasks. Existing retrieval-augmented generation (RAG) methods struggle with noisy augmentation, making it difficult to identify key information and perform global reasoning. StructRAG identifies the optimal structure type for a task, reconstructs documents into this format, and infers answers based on the resulting structure. The framework achieves state-of-the-art performance in various knowledge-intensive tasks, particularly excelling in challenging scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new method to help large language models understand complex information better. Large language models are computers that can read and write text, but they often struggle with complex questions that require understanding many different pieces of information. The authors propose a new way of organizing information called StructRAG, which helps the computer identify what’s important and answer questions correctly. |
Keywords
» Artificial intelligence » Rag » Retrieval augmented generation
