17 citations · 20 across the 8 of their papers we have counts for
8 papers
Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models
Wanlong Liu, Yichen Xiao, Dingyi Zeng +3
Post-Training Quantization (PTQ) is pivotal for deploying large language models (LLMs) within resource-limited settings by significantly reducing resource demands. However, existin…
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
Wanlong Liu, Enqi Zhang, Li Zhou +5
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main…
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Wanlong Liu, Li Zhou, Dingyi Zeng +6
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To addr…
Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
Li Zhou, Taelin Karidi, Wanlong Liu +5
Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our…
MLPs Compass: What is learned when MLPs are combined with PLMs?
Li Zhou, Wenyu Chen, Yong Cao +3
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain deriv…
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Wanlong Liu, Shaohuan Cheng, Dingyi Zeng +1
Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus…
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Wanlong Liu, Dingyi Zeng, Li Zhou +6
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the informat…
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
Li Zhou, Wenyu Chen, Dingyi Zeng +4
Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning o…