Summary of Chain-of-knowledge: Integrating Knowledge Reasoning Into Large Language Models by Learning From Knowledge Graphs, By Yifei Zhang et al.
Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
by Yifei Zhang, Xintao Wang, Jiaqing Liang, Sirui Xia, Lida Chen, Yanghua Xiao
First submitted to arxiv on: 30 Jun 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 Chain-of-Knowledge (CoK), a framework for knowledge reasoning in Large Language Models (LLMs). It addresses the underexplored area of knowledge reasoning in LLMs, which involves deriving new knowledge from existing information. CoK includes methodologies for dataset construction and model learning. The authors create KnowReason, a dataset constructed through rule mining on Knowledge Graphs (KGs), and develop a trial-and-error mechanism to simulate human internal knowledge exploration. They conduct experiments with KnowReason and demonstrate the effectiveness of CoK in refining LLMs for both knowledge reasoning and general reasoning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using big language models to reason and learn new things from what we already know. It’s like how humans explore their own thoughts and ideas. The authors created a special way to do this, called Chain-of-Knowledge (CoK), which includes making datasets and training the model. They tested it with a dataset they made themselves and showed that CoK works well for language models to learn new things. |
