Summary of Kicgpt: Large Language Model with Knowledge in Context For Knowledge Graph Completion, by Yanbin Wei et al.
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
by Yanbin Wei, Qiushi Huang, James T. Kwok, Yu Zhang
First submitted to arxiv on: 4 Feb 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 This paper proposes a novel framework for Knowledge Graph Completion (KGC) that addresses the limitations of existing methods. The framework, called KICGPT, integrates a large language model (LLM) with a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead. KICGPT uses an in-context learning strategy called Knowledge Prompt, which encodes structural knowledge into demonstrations to guide the LLM. The authors demonstrate the effectiveness of KICGPT on benchmark datasets with smaller training overhead and no finetuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers fill in missing information on huge networks of data. This network is called a knowledge graph, and it’s like a big dictionary that helps computers understand relationships between things. Right now, there are many ways to help computers do this, but they all have some drawbacks. Some methods are good for small parts of the graph, but struggle with bigger parts. Others require a lot of extra training or special tuning. This new method, called KICGPT, tries to fix these problems by combining two existing approaches in a clever way. It shows promise on big datasets and requires less work to train. |
Keywords
» Artificial intelligence » Knowledge graph » Large language model » Prompt
