Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Wang, Zichong"'
Large Language Models (LLMs) exhibit impressive problem-solving skills across many tasks, but they still underperform compared to humans in various downstream applications, such as text-to-SQL. On the BIRD benchmark leaderboard, human performance ach
Externí odkaz:
http://arxiv.org/abs/2411.13244
Large Language Models (LLMs) have demonstrated remarkable success across various domains but often lack fairness considerations, potentially leading to discriminatory outcomes against marginalized populations. Unlike fairness in traditional machine l
Externí odkaz:
http://arxiv.org/abs/2408.00992
Autor:
Chinta, Sribala Vidyadhari, Wang, Zichong, Zhang, Xingyu, Viet, Thang Doan, Kashif, Ayesha, Smith, Monique Antoinette, Zhang, Wenbin
Artificial intelligence (AI) is rapidly advancing in healthcare, enhancing the efficiency and effectiveness of services across various specialties, including cardiology, ophthalmology, dermatology, emergency medicine, etc. AI applications have signif
Externí odkaz:
http://arxiv.org/abs/2407.19655
Autor:
Chinta, Sribala Vidyadhari, Wang, Zichong, Yin, Zhipeng, Hoang, Nhat, Gonzalez, Matthew, Quy, Tai Le, Zhang, Wenbin
The integration of Artificial Intelligence (AI) into education has transformative potential, providing tailored learning experiences and creative instructional approaches. However, the inherent biases in AI algorithms hinder this improvement by unint
Externí odkaz:
http://arxiv.org/abs/2407.18745
Language Models (LMs) have demonstrated exceptional performance across various Natural Language Processing (NLP) tasks. Despite these advancements, LMs can inherit and amplify societal biases related to sensitive attributes such as gender and race, l
Externí odkaz:
http://arxiv.org/abs/2407.18454
In the rapidly evolving landscape of generative artificial intelligence (AI), the increasingly pertinent issue of copyright infringement arises as AI advances to generate content from scraped copyrighted data, prompting questions about ownership and
Externí odkaz:
http://arxiv.org/abs/2404.08221
Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may
Externí odkaz:
http://arxiv.org/abs/2404.01349
Language models serve as a cornerstone in natural language processing (NLP), utilizing mathematical methods to generalize language laws and knowledge for prediction and generation. Over extensive research spanning decades, language modeling has progr
Externí odkaz:
http://arxiv.org/abs/2402.06853
Autor:
Wang, Zichong, Zhou, Yang, Qiu, Meikang, Haque, Israat, Brown, Laura, He, Yi, Wang, Jianwu, Lo, David, Zhang, Wenbin
The increasing use of Machine Learning (ML) software can lead to unfair and unethical decisions, thus fairness bugs in software are becoming a growing concern. Addressing these fairness bugs often involves sacrificing ML performance, such as accuracy
Externí odkaz:
http://arxiv.org/abs/2302.08018
Autor:
Wang, Zichong, Saxena, Nripsuta, Yu, Tongjia, Karki, Sneha, Zetty, Tyler, Haque, Israat, Zhou, Shan, Kc, Dukka, Stockwell, Ian, Bifet, Albert, Zhang, Wenbin
Bias in machine learning has rightly received significant attention over the last decade. However, most fair machine learning (fair-ML) work to address bias in decision-making systems has focused solely on the offline setting. Despite the wide preval
Externí odkaz:
http://arxiv.org/abs/2302.08017