Summary of Kag: Boosting Llms in Professional Domains Via Knowledge Augmented Generation, by Lei Liang et al.
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
by Lei Liang, Mengshu Sun, Zhengke Gui, Zhongshu Zhu, Zhouyu Jiang, Ling Zhong, Yuan Qu, Peilong Zhao, Zhongpu Bo, Jin Yang, Huaidong Xiong, Lin Yuan, Jun Xu, Zaoyang Wang, Zhiqiang Zhang, Wen Zhang, Huajun Chen, Wenguang Chen, Jun Zhou
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper introduces a new framework called Knowledge Augmented Generation (KAG) to improve the efficiency and effectiveness of professional knowledge services. KAG addresses limitations in retrieval-augmented generation (RAG) technology by leveraging large language models (LLMs), knowledge graphs (KGs), and logical-form-guided hybrid reasoning engines. The framework’s five key aspects include LLM-friendly knowledge representation, mutual-indexing between KGs and original chunks, logical-form-guided hybrid reasoning, knowledge alignment with semantic reasoning, and model capability enhancement for KAG. Compared to existing RAG methods, KAG outperforms state-of-the-art models in multihop question answering by achieving a relative improvement of 19.6% on 2wiki and 33.5% on hotpotQA in terms of F1 score. The paper demonstrates the success of KAG in professional knowledge Q&A tasks, such as E-Government Q&A and E-Health Q&A. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computers understand and generate professional knowledge better. Right now, there’s a gap between what computers can do and what humans know, so this framework aims to bridge that gap. It uses special tools like large language models and knowledge graphs to improve the computer’s ability to reason and generate answers. The results show that this new approach is much better than existing methods at answering complex questions. The researchers also tested it in real-world scenarios and found that it can be used to improve professional knowledge Q&A services. |
Keywords
» Artificial intelligence » Alignment » F1 score » Question answering » Rag » Retrieval augmented generation
