8 citations · 13 across the 8 of their papers we have counts for
8 papers
Finding the Cracks: Improving LLMs Reasoning with Paraphrastic Probing and Consistency Verification
Weili Shi, Dongliang Guo, Lehan Yang +3
Large language models have demonstrated impressive performance across a variety of reasoning tasks. However, their problem-solving ability often declines on more complex tasks due…
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
Dongliang Guo, Mengxuan Hu, Zihan Guan +2
Large multi-modal models inevitably decay over time as facts update and previously learned information becomes outdated. Traditional approaches such as fine-tuning are often imprac…
Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing
Dongliang Guo, Mengxuan Hu, Zihan Guan +3
Large pre-trained models have achieved notable success across a range of downstream tasks. However, recent research shows that a type of adversarial attack ( backdo…
No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users
Mengxuan Hu, Hongyi Wu, Zihan Guan +4
Retrieval-Augmented Generation (RAG) is widely adopted for its effectiveness and cost-efficiency in mitigating hallucinations and enhancing the domain-specific generation capabilit…
Bridging the Fairness Divide: Achieving Group and Individual Fairness in Graph Neural Networks
Duna Zhan, Dongliang Guo, Pengsheng Ji +1
Graph neural networks (GNNs) have emerged as a powerful tool for analyzing and learning from complex data structured as graphs, demonstrating remarkable effectiveness in various ap…
XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications
Zhongliang Zhou, Mengxuan Hu, Mariah Salcedo +7
Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing co…
Trustworthy Representation Learning Across Domains
Ronghang Zhu, Dongliang Guo, Daiqing Qi +3
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suff…
Fair Attribute Completion on Graph with Missing Attributes
Dongliang Guo, Zhixuan Chu, Sheng Li
Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair gra…