Summary of Large Language Models-guided Dynamic Adaptation For Temporal Knowledge Graph Reasoning, by Jiapu Wang et al.
Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoning
by Jiapu Wang, Kai Sun, Linhao Luo, Wei Wei, Yongli Hu, Alan Wee-Chung Liew, Shirui Pan, Baocai Yin
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Large Language Models-guided Dynamic Adaptation (LLM-DA) method for Temporal Knowledge Graph Reasoning (TKGR). The approach leverages LLMs to analyze historical data and extract temporal logical rules, which reveal temporal patterns and enable interpretable reasoning. To accommodate the evolving nature of TKGRs, a dynamic adaptation strategy updates the LLM-generated rules with the latest events, ensuring that they incorporate the most recent knowledge and generalize well to future events. Experimental results demonstrate significant accuracy improvements over several common datasets without requiring fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand complex relationships in Temporal Knowledge Graphs by using language models. Current methods for this are either deep learning-based, which are hard to understand, or rule-based, which struggle to learn patterns. Language models have shown great ability in temporal reasoning, but they’re like black boxes, so we can’t see how they work. And updating them is too time-consuming. The proposed LLM-DA method uses language models to find rules that reveal patterns and make predictions more accurate. This approach adapts quickly to new information, making it a powerful tool for understanding complex relationships in the future. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Knowledge graph
