68 citations · 120 across the 10 of their papers we have counts for
10 papers
To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
Bozhong Tian, Xiaozhuan Liang, Siyuan Cheng +6
Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in kn…
InstructEdit: Instruction-based Knowledge Editing for Large Language Models
Ningyu Zhang, Bozhong Tian, Siyuan Cheng +6
Knowledge editing for large language models can offer an efficient solution to alter a model's behavior without negatively impacting the overall performance. However, the current a…
Can We Edit Multimodal Large Language Models?
Siyuan Cheng, Bozhong Tian, Qingbin Liu +4
In this paper, we focus on editing Multimodal Large Language Models (MLLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a hi…
Unveiling the Pitfalls of Knowledge Editing for Large Language Models
Zhoubo Li, Ningyu Zhang, Yunzhi Yao +3
As the cost associated with fine-tuning Large Language Models (LLMs) continues to rise, recent research efforts have pivoted towards developing methodologies to edit implicit knowl…
Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction
Yunzhi Yao, Shengyu Mao, Ningyu Zhang +4
With the development of pre-trained language models, many prompt-based approaches to data-efficient knowledge graph construction have been proposed and achieved impressive performa…
Towards Realistic Low-resource Relation Extraction: A Benchmark with Empirical Baseline Study
Xin Xu, Xiang Chen, Ningyu Zhang +3
This paper presents an empirical study to build relation extraction systems in low-resource settings. Based upon recent pre-trained language models, we comprehensively investigate…
Ontology-enhanced Prompt-tuning for Few-shot Learning
Hongbin Ye, Ningyu Zhang, Shumin Deng +5
Few-shot Learning (FSL) is aimed to make predictions based on a limited number of samples. Structured data such as knowledge graphs and ontology libraries has been leveraged to ben…
Interventional Aspect-Based Sentiment Analysis
Zhen Bi, Ningyu Zhang, Ganqiang Ye +3
Recent neural-based aspect-based sentiment analysis approaches, though achieving promising improvement on benchmark datasets, have reported suffering from poor robustness when enco…
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Ningyu Zhang, Shumin Deng, Zhanlin Sun +4
We propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate "…
Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
Ningyu Zhang, Shumin Deng, Zhanlin Sun +3
A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for…