87 citations · 143 across the 13 of their papers we have counts for
14 papers
EvalBuilder: Can LLMs Build Efficient Benchmarks?
Huaisheng Zhu, Hangfan Zhang, Teng Xiao +5
Large language model (LLM) agents are increasingly used for long-horizon research and engineering, yet evaluating these capabilities remains expensive, time-consuming, and heavily…
Do Audio LLMs Really LISTEN, or Just Transcribe? Measuring Lexical vs. Acoustic Emotion Cues Reliance
Jingyi Chen, Zhimeng Guo, Jiyun Chun +3
Understanding emotion from speech requires sensitivity to both lexical and acoustic cues. However, it remains unclear whether large audio language models (LALMs) genuinely process…
The Path of Self-Evolving Large Language Models: Achieving Data-Efficient Learning via Intrinsic Feedback
Hangfan Zhang, Siyuan Xu, Zhimeng Guo +8
Reinforcement learning (RL) has demonstrated potential in enhancing the reasoning capabilities of large language models (LLMs), but such training typically demands substantial effo…
Simple Denoising Diffusion Language Models
Huaisheng Zhu, Zhengyu Chen, Shijie Zhou +8
Recent Uniform State Diffusion Models (USDMs), initialized from a uniform prior, offer the promise of fast text generation due to their inherent self-correction ability compared to…
Optimizing Token Choice for Code Watermarking: An RL Approach
Zhimeng Guo, Huaisheng Zhu, Siyuan Xu +3
Protecting intellectual property on LLM-generated code necessitates effective watermarking systems that can operate within code's highly structured, syntactically constrained natur…
Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark
Xiaowei Qian, Zhimeng Guo, Jialiang Li +4
Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies o…
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused?
Hangfan Zhang, Zhimeng Guo, Huaisheng Zhu +5
Large Language Models (LLMs) have achieved unprecedented performance in Natural Language Generation (NLG) tasks. However, many existing studies have shown that they could be misuse…
Towards Fair Graph Neural Networks via Graph Counterfactual
Zhimeng Guo, Jialiang Li, Teng Xiao +2
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent w…
Fairness-aware Message Passing for Graph Neural Networks
Huaisheng Zhu, Guoji Fu, Zhimeng Guo +3
Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in r…
Counterfactual Learning on Graphs: A Survey
Zhimeng Guo, Teng Xiao, Zongyu Wu +3
Graph-structured data are pervasive in the real-world such as social networks, molecular graphs and transaction networks. Graph neural networks (GNNs) have achieved great success i…
Link Prediction on Heterophilic Graphs via Disentangled Representation Learning
Shijie Zhou, Zhimeng Guo, Charu Aggarwal +2
Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homo…
Decoupled Self-supervised Learning for Non-Homophilous Graphs
Teng Xiao, Zhengyu Chen, Zhimeng Guo +2
This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is…
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu +5
Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applica…
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Enyan Dai, Shijie Zhou, Zhimeng Guo +1
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophil…