Summary of Chain Of History: Learning and Forecasting with Llms For Temporal Knowledge Graph Completion, by Ruilin Luo et al.
Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion
by Ruilin Luo, Tianle Gu, Haoling Li, Junzhe Li, Zicheng Lin, Jiayi Li, Yujiu Yang
First submitted to arxiv on: 11 Jan 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 presents an innovative approach to Temporal Knowledge Graph Completion (TKGC) by leveraging Large Language Models (LLMs) for reasoning. The authors propose a pipeline that can be easily transferred across different datasets, highlighting the strengths of LLMs in discerning structural information from historical chains. They also explore the impact of various factors on LLM performance, including reverse logic comprehension and parameter-efficient fine-tuning. The results show that their framework outperforms or matches existing methods on several popular metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to use big language models to predict missing information in timelines. It shows how these models can learn from what we already know about the past to make better predictions about the future. By fine-tuning the models and exploring their strengths and weaknesses, researchers can create more accurate and reliable tools for working with timeline data. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph » Parameter efficient
