1 citations · 3 across the 10 of their papers we have counts for
11 papers
Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations
Li Zhou, Hao Jiang, Junjie Li +4
Explicit structural information has been proven to be encoded by Graph Neural Networks (GNNs), serving as auxiliary knowledge to enhance model capabilities and improve performance…
QFFT, Question-Free Fine-Tuning for Adaptive Reasoning
Wanlong Liu, Junxiao Xu, Fei Yu +7
Recent advancements in Long Chain-of-Thought (CoT) reasoning models have improved performance on complex tasks, but they suffer from overthinking, which generates redundant reasoni…
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…
RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions
Wanlong Liu, Junying Chen, Ke Ji +3
Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face…
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…
Rethinking Relation Classification with Graph Meaning Representations
Li Zhou, Wenyu Chen, Dingyi Zeng +2
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the growin…
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features
Li Zhou, Antonia Karamolegkou, Wenyu Chen +1
The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rel…
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…