Summary of Think-on-graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation, by Shengjie Ma et al.
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
by Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, Jian Guo
First submitted to arxiv on: 15 Jul 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 paper introduces Think-on-Graph 2.0 (ToG-2), a hybrid retrieval-augmented generation framework that leverages knowledge graphs and unstructured documents to improve the depth and completeness of retrieved information for complex reasoning tasks. ToG-2 iteratively retrieves information from both sources in a tight-coupling manner, utilizing knowledge graphs to link documents via entities and facilitating deep context retrieval. The framework alternates between graph retrieval and context retrieval to search for in-depth clues relevant to the question, enabling language models to generate answers. Experiments demonstrate that ToG-2 achieves state-of-the-art performance on 6 out of 7 knowledge-intensive datasets with GPT-3.5 and can elevate the performance of smaller models to the level of direct reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ToG-2 is a new way for computers to understand and answer complex questions by combining information from different sources, like books and websites. It works by looking at connections between words and ideas in these sources, which helps it find more accurate and detailed answers. This approach is especially helpful when the question requires understanding multiple related concepts. The researchers tested ToG-2 with several language models and found that it outperformed them on many tasks. |
Keywords
» Artificial intelligence » Gpt » Retrieval augmented generation
