38 citations · 72 across the 18 of their papers we have counts for
19 papers
Do Agent Benchmarks Do What They Say? An Executable-Contract Audit of Tool-Using Agent Environments
Rohith Reddy Bellibatlu, Zichong Wang, Wenbin Zhang
Tool-using agents are entering settings where a wrong action carries real cost, and the benchmarks certifying them grade what each simulated tool call reports having done, assuming…
FairLMs: A Turnkey Library for Fairness in Language Models
Jiale Zhang, Michael Larionov, Zichong Wang +2
Fairness research on language models involves measuring bias, applying mitigation methods, and examining the evidence on which an evaluation rests. Existing tools offer complementa…
JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems
Rohith Reddy Bellibatlu, Edward Raff, Wenbin Zhang
Large language models are widely used to judge the output of other language models, yet whether a judge returns the same verdict when the same request is worded differently remains…
Fairness-Aware Graph Representation Learning with Limited Demographic Information
Zichong Wang, Zhipeng Yin, Liping Yang +4
Ensuring fairness in Graph Neural Networks is fundamental to promoting trustworthy and socially responsible machine learning systems. In response, numerous fair graph learning meth…
AI Fairness Beyond Complete Demographics: Current Achievements and Future Directions
Zichong Wang, Zhipeng Yin, Roland H. C. Yap +1
Fairness in artificial intelligence (AI) has become a growing concern due to discriminatory outcomes in AI-based decision-making systems. While various methods have been proposed t…
Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
Shamim Yazdani, Akansha Singh, Nripsuta Saxena +6
In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been…
Towards Transparent AI: A Survey on Explainable Language Models
Avash Palikhe, Zichong Wang, Zhipeng Yin +4
Language Models (LMs) have significantly advanced natural language processing and enabled remarkable progress across diverse domains, yet their black-box nature raises critical con…
AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Models
Zhipeng Yin, Zichong Wang, Avash Palikhe +3
Generative models have achieved impressive results in text to image tasks, significantly advancing visual content creation. However, this progress comes at a cost, as such models r…
Bridging Neural Networks and Dynamic Time Warping for Adaptive Time Series Classification
Jintao Qu, Zichong Wang, Chenhao Wu +1
Neural networks have achieved remarkable success in time series classification, but their reliance on large amounts of labeled data for training limits their applicability in cold-…
Towards Transparent AI: A Survey on Explainable Large Language Models
Avash Palikhe, Zhenyu Yu, Zichong Wang +1
Large Language Models (LLMs) have played a pivotal role in advancing Artificial Intelligence (AI). However, despite their achievements, LLMs often struggle to explain their decisio…
Datasets for Fairness in Language Models: An In-Depth Survey
Jiale Zhang, Zichong Wang, Avash Palikhe +2
Despite the growing reliance on fairness benchmarks to evaluate language models, the datasets that underpin these benchmarks remain critically underexamined. This survey addresses…
Fairness in Large Language Models in Three Hours
Thang Doan Viet, Zichong Wang, Minh Nhat Nguyen +1
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes agai…
AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias
Sribala Vidyadhari Chinta, Zichong Wang, Avash Palikhe +5
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmol…
FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications
Zhipeng Yin, Sribala Vidyadhari Chinta, Zichong Wang +2
The integration of AI in education holds immense potential for personalizing learning experiences and transforming instructional practices. However, AI systems can inadvertently en…
Fairness Definitions in Language Models Explained
Zhipeng Yin, Zichong Wang, Avash Palikhe +1
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify soci…
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection
Saket S. Chaturvedi, Lan Zhang, Wenbin Zhang +2
3D object detection plays an important role in autonomous driving; however, its vulnerability to backdoor attacks has become evident. By injecting ''triggers'' to poison the traini…
Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AI
Archer Amon, Zhipeng Yin, Zichong Wang +2
Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent con…
Fairness in Large Language Models: A Taxonomic Survey
Zhibo Chu, Zichong Wang, Wenbin Zhang
Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of…
History, Development, and Principles of Large Language Models-An Introductory Survey
Zichong Wang, Zhibo Chu, Thang Viet Doan +3
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation.…