Summary of Knowledge Pyramid Construction For Multi-level Retrieval-augmented Generation, by Rubing Chen et al.
Knowledge Pyramid Construction for Multi-Level Retrieval-Augmented Generation
by Rubing Chen, Xulu Zhang, Jiaxin Wu, Wenqi Fan, Xiao-Yong Wei, Qing Li
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A Retrieval-Augmented Generation (RAG) framework is enhanced with a multi-layer knowledge pyramid approach to balance precision and recall in question-answering tasks. The proposed PolyRAG method consists of Ontologies, Knowledge Graphs (KGs), and chunk-based raw text, leveraging cross-layer augmentation and filtering techniques for comprehensive knowledge coverage and condensation. Two domain-specific benchmarks are introduced, with the proposed method outperforming 19 state-of-the-art methods in comprehensive experiments. Notably, PolyRAG improves GPT-4’s performance by 395% F1 gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers answer questions more accurately. It combines different types of knowledge to find answers. The new approach, called PolyRAG, is tested on two special areas: academics and finance. PolyRAG does better than other methods in finding answers. It even makes a language model, GPT-4, much better at answering questions. |
Keywords
» Artificial intelligence » Gpt » Language model » Precision » Question answering » Rag » Recall » Retrieval augmented generation
