1 citations · 1 across the 7 of their papers we have counts for
7 papers
GraspLLM: Towards Zero-Shot Generalization on Text-Attributed Graphs with LLMs
Hengyi Feng, Zeang Sheng, Meiyi Qiang +2
Research on Text-Attributed Graphs (TAGs) has gained significant attention recently due to its broad applications across various real-world data scenarios, such as citation network…
TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
Hengyi Feng, Hao Liang, Mingrui Chen +6
Real-world audio-visual understanding requires chaining evidence that is sparse, temporally dispersed, and split across the visual and auditory streams, whereas existing benchmarks…
One-Eval: An Agentic System for Automated and Traceable LLM Evaluation
Chengyu Shen, Yanheng Hou, Minghui Pan +8
Reliable evaluation is essential for developing and deploying large language models, yet in practice it often requires substantial manual effort: practitioners must identify approp…
Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval?
Hengyi Feng, Zeang Sheng, Meiyi Qiang +2
Despite the remarkable success of multimodal large language models (MLLMs) in generative tasks, we observe that they exhibit a counterintuitive deficiency in the zero-shot multimod…
Towards Scalable and Deep Graph Neural Networks via Noise Masking
Yuxuan Liang, Wentao Zhang, Zeang Sheng +5
In recent years, Graph Neural Networks (GNNs) have achieved remarkable success in many graph mining tasks. However, scaling them to large graphs is challenging due to the high comp…
Can LLMs be Good Graph Judge for Knowledge Graph Construction?
Haoyu Huang, Chong Chen, Zeang Sheng +2
In real-world scenarios, most of the data obtained from the information retrieval (IR) system is unstructured. Converting natural language sentences into structured Knowledge Graph…
Acceleration Algorithms in GNNs: A Survey
Lu Ma, Zeang Sheng, Xunkai Li +5
Graph Neural Networks (GNNs) have demonstrated effectiveness in various graph-based tasks. However, their inefficiency in training and inference presents challenges for scaling up…