Summary of Easyrag: Efficient Retrieval-augmented Generation Framework For Automated Network Operations, by Zhangchi Feng et al.
EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
by Zhangchi Feng, Dongdong Kuang, Zhongyuan Wang, Zhijie Nie, Yaowei Zheng, Richong Zhang
First submitted to arxiv on: 14 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 The EasyRAG framework is a lightweight and efficient retrieval-augmented generation framework for automated network operations. It features a straightforward approach that includes data processing, dual-route sparse retrieval, LLM Reranker reranking, and LLM answer generation and optimization. This approach achieved first place in the GLM4 track in the preliminary round and second place in the semifinals. The framework is simple to deploy, requiring minimal VRAM and no fine-tuning of models, making it highly scalable and easy to customize. Additionally, the framework features an efficient inference acceleration scheme that significantly reduces latency while maintaining accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EasyRAG is a new way for computers to understand questions and answer them correctly. It’s like having a super-smart computer assistant! The team designed a special process that uses different parts of the brain (LLM) to figure out what the question means, find the right answer, and make sure it’s correct. This approach worked really well in tests and even won awards! What makes EasyRAG special is how easy it is to use – you don’t need to train any special models or buy a lot of memory (VRAM). Plus, you can customize it to fit your specific needs. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Optimization » Retrieval augmented generation
