1 citations · 4 across the 5 of their papers we have counts for
5 papers
Towards Continual Knowledge Graph Embedding via Incremental Distillation
Jiajun Liu, Wenjun Ke, Peng Wang +5
Traditional knowledge graph embedding (KGE) methods typically require preserving the entire knowledge graph (KG) with significant training costs when new knowledge emerges. To addr…
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Guozheng Li, Peng Wang, Wenjun Ke +5
Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL…
Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors
Guozheng Li, Peng Wang, Jiajun Liu +4
Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-cont…
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
Guozheng Li, Wenjun Ke, Peng Wang +5
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompt…
Refinement Checking for Multirate Hybrid ZIA
Guozheng Li, Zining Cao, Zheng Gao
A hybrid system is a dynamical system with both discrete and continuous components. In order to study the modeling and verification aspects of hybrid system, in this paper we first…