Summary of Kg-fit: Knowledge Graph Fine-tuning Upon Open-world Knowledge, by Pengcheng Jiang et al.
KG-FIT: Knowledge Graph Fine-Tuning Upon Open-World Knowledge
by Pengcheng Jiang, Lang Cao, Cao Xiao, Parminder Bhatia, Jimeng Sun, Jiawei Han
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 introduces a novel approach called KG-FIT that leverages large language model (LLM) guided refinement to construct a hierarchical structure of entity clusters within a knowledge graph. By incorporating this hierarchical knowledge along with textual information during fine-tuning, KG-FIT effectively captures both global semantics from the LLM and local semantics from the KG. The authors demonstrate the superiority of KG-FIT over state-of-the-art pre-trained language model-based methods on benchmark datasets FB15K-237, YAGO3-10, and PrimeKG, achieving improvements in the Hits@10 metric for the link prediction task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make sense of big amounts of information from the internet. It’s called KG-FIT, and it uses two things: big language models that understand words, and special graphs that connect ideas. The paper shows how combining these two things makes better results for understanding relationships between different pieces of information. |
Keywords
» Artificial intelligence » Fine tuning » Knowledge graph » Language model » Large language model » Semantics
