1.1k citations · 2.3k across the 68 of their papers we have counts for
24 papers · 1 filter
Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate
Mingqing Zhang, Haisong Gong, Qiang Liu +2
The rapid spread of rumors on social media platforms during breaking events severely hinders the dissemination of the truth. Previous studies reveal that the lack of annotated reso…
Bi-Level Graph Structure Learning for Next POI Recommendation
Liang Wang, Shu Wu, Qiang Liu +3
Next point-of-interest (POI) recommendation aims to predict a user's next destination based on sequential check-in history and a set of POI candidates. Graph neural networks (GNNs)…
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
Liang Wang, Qiang Liu, Shaozhen Liu +2
Molecular property prediction (MPP) is integral to drug discovery and material science, but often faces the challenge of data scarcity in real-world scenarios. Addressing this, few…
GOT4Rec: Graph of Thoughts for Sequential Recommendation
Zewen Long, Liang Wang, Shu Wu +1
With their vast open-world knowledge and reasoning abilities, large language models (LLMs) have become a promising tool for sequential recommendation. Researchers have explored var…
Beyond Filtering: Adaptive Image-Text Quality Enhancement for MLLM Pretraining
Han Huang, Yuqi Huo, Zijia Zhao +6
Multimodal large language models (MLLMs) have made significant strides by integrating visual and textual modalities. A critical factor in training MLLMs is the quality of image-tex…
Uncovering Overfitting in Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Knowledge editing has been proposed as an effective method for updating and correcting the internal knowledge of Large Language Models (LLMs). However, existing editing methods oft…
Enhancing Multi-hop Reasoning through Knowledge Erasure in Large Language Model Editing
Mengqi Zhang, Bowen Fang, Qiang Liu +4
Large language models (LLMs) face challenges with internal knowledge inaccuracies and outdated information. Knowledge editing has emerged as a pivotal approach to mitigate these is…
DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
Xin Sun, Liang Wang, Qiang Liu +2
This paper addresses the challenge of out-of-distribution (OOD) generalization in graph machine learning, a field rapidly advancing yet grappling with the discrepancy between sourc…
Modality-Balanced Learning for Multimedia Recommendation
Jinghao Zhang, Guofan Liu, Qiang Liu +2
Many recommender models have been proposed to investigate how to incorporate multimodal content information into traditional collaborative filtering framework effectively. The use…
Navigating the Noisy Crowd: Finding Key Information for Claim Verification
Haisong Gong, Huanhuan Ma, Qiang Liu +2
Claim verification is a task that involves assessing the truthfulness of a given claim based on multiple evidence pieces. Using large language models (LLMs) for claim verification…
Interpretable Multimodal Out-of-context Detection with Soft Logic Regularization
Huanhuan Ma, Jinghao Zhang, Qiang Liu +2
The rapid spread of information through mobile devices and media has led to the widespread of false or deceptive news, causing significant concerns in society. Among different type…
Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
Xiang Tao, Liang Wang, Qiang Liu +1
Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Recently, numerous rumor detection models which utilize textual info…
Out-of-distribution Rumor Detection via Test-Time Adaptation
Xiang Tao, Mingqing Zhang, Qiang Liu +2
Due to the rapid spread of rumors on social media, rumor detection has become an extremely important challenge. Existing methods for rumor detection have achieved good performance,…
Bridging Text and Molecule: A Survey on Multimodal Frameworks for Molecule
Yi Xiao, Xiangxin Zhou, Qiang Liu +1
Artificial intelligence has demonstrated immense potential in scientific research. Within molecular science, it is revolutionizing the traditional computer-aided paradigm, ushering…
VLKEB: A Large Vision-Language Model Knowledge Editing Benchmark
Han Huang, Haitian Zhong, Tao Yu +4
Recently, knowledge editing on large language models (LLMs) has received considerable attention. Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challeng…
Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social Media
Jiajun Zhang, Zhixun Li, Qiang Liu +2
With the rapid development of social media, the wide dissemination of fake news on social media is increasingly threatening both individuals and society. One of the unique challeng…
DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
Jiaqi Guan, Xiangxin Zhou, Yuwei Yang +6
Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignor…
Knowledge Graph Enhanced Large Language Model Editing
Mengqi Zhang, Xiaotian Ye, Qiang Liu +3
Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing…
Text-Guided Molecule Generation with Diffusion Language Model
Haisong Gong, Qiang Liu, Shu Wu +1
Text-guided molecule generation is a task where molecules are generated to match specific textual descriptions. Recently, most existing SMILES-based molecule generation methods rel…
Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
Haisong Gong, Weizhi Xu, Shu wu +2
Fact checking aims to predict claim veracity by reasoning over multiple evidence pieces. It usually involves evidence retrieval and veracity reasoning. In this paper, we focus on t…
Rethinking Graph Masked Autoencoders through Alignment and Uniformity
Liang Wang, Xiang Tao, Qiang Liu +1
Self-supervised learning on graphs can be bifurcated into contrastive and generative methods. Contrastive methods, also known as graph contrastive learning (GCL), have dominated gr…
Can Large Language Models Detect Rumors on Social Media?
Qiang Liu, Xiang Tao, Junfei Wu +2
In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media. However, it is challenging for LLMs to reason over the entire propagation info…
Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting
Yuwei Xia, Ding Wang, Qiang Liu +3
Temporal Knowledge Graph (TKG) forecasting aims to predict future facts based on given histories. Most recent graph-based models excel at capturing structural information within TK…
Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models
Junfei Wu, Qiang Liu, Ding Wang +4
Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that t…