Publications (21)
LLM as GNN: Graph Vocabulary Learning for Text-Attributed Graph Foundation Models
Xi Zhu, Haochen Xue, Ziwei Zhao +7
Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain…
Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis
Yulong Li, Zhixiang Lu, Feilong Tang +8
The rapid development of social media has significantly reshaped the dynamics of public opinion, resulting in complex interactions that traditional models fail to effectively captu…
LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions
Haochen Xue, Chenghao Liu, Chong Zhang +9
Accurately forecasting electricity price volatility is crucial for effective risk management and decision-making. Traditional forecasting models often fall short in capturing the c…
MSWAL: 3D Multi-class Segmentation of Whole Abdominal Lesions Dataset
Zhaodong Wu, Qiaochu Zhao, Ming Hu +13
With the significantly increasing incidence and prevalence of abdominal diseases, there is a need to embrace greater use of new innovations and technology for the diagnosis and tre…
A Simple and Effective Baseline for Attentional Generative Adversarial Networks
Mingyu Jin, Chong Zhang, Qinkai Yu +3
Synthesising a text-to-image model of high-quality images by guiding the generative model through the Text description is an innovative and challenging task. In recent years, AttnG…
RAGRouter-Bench: A Dataset and Benchmark for Adaptive RAG Routing
Ziqi Wang, Xi Zhu, Shuhang Lin +3
Retrieval-augmented generation (RAG) has evolved into a family of paradigms with distinct performance profiles and resource demands, turning paradigm selection into a multi-criteri…
TAGS: A Test-Time Generalist-Specialist Framework with Retrieval-Augmented Reasoning and Verification
Jianghao Wu, Feilong Tang, Yulong Li +5
Recent advances such as Chain-of-Thought prompting have significantly improved large language models (LLMs) in zero-shot medical reasoning. However, prompting-based methods often r…
ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering
Zhaodong Wu, Haochen Xue, Qi Cao +5
Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable f…
Beyond Words: AuralLLM and SignMST-C for Sign Language Production and Bidirectional Accessibility
Yulong Li, Yuxuan Zhang, Feilong Tang +10
Sign language is the primary communication mode for 72 million hearing-impaired individuals worldwide, necessitating effective bidirectional Sign Language Production and Sign Langu…
Goal-guided Generative Prompt Injection Attack on Large Language Models
Chong Zhang, Mingyu Jin, Qinkai Yu +3
Current large language models (LLMs) provide a strong foundation for large-scale user-oriented natural language tasks. A large number of users can easily inject adversarial text or…
Node-as-Agent: Graph Agentic Network
Minghao Guo, Xi Zhu, Qingyue Jiao +7
Graph Neural Networks (GNNs) have achieved remarkable success in graph-based learning by propagating information among neighbor nodes via predefined aggregation mechanisms. However…
ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction
Mingyu Jin, Haochen Xue, Zhenting Wang +5
The prediction of protein-protein interactions (PPIs) is crucial for understanding biological functions and diseases. Previous machine learning approaches to PPI prediction mainly…
Semantic-Preserving Prompt Hijacking: A Black-Box Adversarial Attack on Auto-Prompt Optimization
Chong Zhang, Xiang Li, Jia Wang +3
LLMs increasingly integrate auto-suggestion optimization modules, enabling them to rewrite and display user input before generating the final response. While this design aims to en…
Multi-task Prompt Words Learning for Social Media Content Generation
Haochen Xue, Chong Zhang, Chengzhi Liu +2
The rapid development of the Internet has profoundly changed human life. Humans are increasingly expressing themselves and interacting with others on social media platforms. Howeve…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Towards Efficient Medical Reasoning with Minimal Fine-Tuning Data
Xinlin Zhuang, Feilong Tang, Haolin Yang +9
Supervised Fine-Tuning (SFT) of the language backbone plays a pivotal role in adapting Vision-Language Models (VLMs) to specialized domains such as medical reasoning. However, exis…
Phenome-Wide Multi-Omics Integration Uncovers Distinct Archetypes of Human Aging
Huifa Li, Feilong Tang, Haochen Xue +5
Aging is a highly complex and heterogeneous process that progresses at different rates across individuals, making biological age (BA) a more accurate indicator of physiological dec…
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Haochen Xue, Feilong Tang, Ming Hu +13
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…
Image Blending Algorithm with Automatic Mask Generation
Haochen Xue, Mingyu Jin, Chong Zhang +3
In recent years, image blending has gained popularity for its ability to create visually stunning content. However, the current image blending algorithms mainly have the following…
Bridging the Projection Gap: Overcoming Projection Bias Through Parameterized Distance Learning
Chong Zhang, Mingyu Jin, Qinkai Yu +3
Generalized zero-shot learning (GZSL) aims to recognize samples from both seen and unseen classes using only seen class samples for training. However, GZSL methods are prone to bia…
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities
Wenyue Hua, Kaijie Zhu, Lingyao Li +7
This study intends to systematically disentangle pure logic reasoning and text understanding by investigating the contrast across abstract and contextualized logical problems from…