Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024.findings-eacl)
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| Challenge: | Existing knowledge graph completion models lack textual information, which limits their performance . a plug-in-and-play approach is needed to train small models in descriptive context . |
| Approach: | They propose a plug-in-and-play approach to knowledge graph completion that prompts LLMs to generate descriptive context. |
| Outcome: | The proposed method improves performance on Wikipedia articles and synset definitions. |
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
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| Challenge: | Empirical evidence suggests that LLMs perform worse than conventional KGC approaches. |
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| Challenge: | Existing knowledge graph completion methods ignore inconsistent representation spaces between natural language and graph structures, leading to duplicate works and time-consuming processes. |
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| Challenge: | Pre-trained language models have achieved remarkable knowledge graph completion (KGC) success. |
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| Challenge: | Knowledge distillation is a major technique for deploying vast language models in resource-strapped environments. |
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