8 papers
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…
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…
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…
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…
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…