1 citations · 4 across the 7 of their papers we have counts for
7 papers
Unlearning of Knowledge Graph Embedding via Preference Optimization
Jiajun Liu, Wenjun Ke, Peng Wang +5
Existing knowledge graphs (KGs) inevitably contain outdated or erroneous knowledge that needs to be removed from knowledge graph embedding (KGE) models. To address this challenge,…
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…
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…
Empirical Analysis of Dialogue Relation Extraction with Large Language Models
Guozheng Li, Zijie Xu, Ziyu Shang +3
Dialogue relation extraction (DRE) aims to extract relations between two arguments within a dialogue, which is more challenging than standard RE due to the higher person pronoun fr…
CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News
Mengna Zhu, Zijie Xu, Kaisheng Zeng +4
Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis…