3 citations · 6 across the 8 of their papers we have counts for
6 papers · 1 filter
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification
Ke Ji, Peng Wang, Wenjun Ke +4
Recently, various pre-trained language models (PLMs) have been proposed to prove their impressive performances on a wide range of few-shot tasks. However, limited by the unstructur…
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
Jiajun Liu, Wenjun Ke, Peng Wang +7
Continual Knowledge Graph Embedding (CKGE) aims to efficiently learn new knowledge and simultaneously preserve old knowledge. Dominant approaches primarily focus on alleviating cat…
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…