Generative AI Model
Generative AI models are computational systems designed to create new content, such as text, images, and audio, by learning patterns from existing data. Current research emphasizes improving efficiency and scalability of these models, particularly focusing on architectures like transformers and diffusion models, and addressing challenges like bias mitigation, data security, and responsible AI practices. The widespread adoption of generative AI across diverse fields, from medicine and law to art and entertainment, necessitates rigorous research into its capabilities, limitations, and societal impact.
Papers
Near to Mid-term Risks and Opportunities of Open-Source Generative AI
Francisco Eiras, Aleksandar Petrov, Bertie Vidgen, Christian Schroeder de Witt, Fabio Pizzati, Katherine Elkins, Supratik Mukhopadhyay, Adel Bibi, Botos Csaba, Fabro Steibel, Fazl Barez, Genevieve Smith, Gianluca Guadagni, Jon Chun, Jordi Cabot, Joseph Marvin Imperial, Juan A. Nolazco-Flores, Lori Landay, Matthew Jackson, Paul Röttger, Philip H. S. Torr, Trevor Darrell, Yong Suk Lee, Jakob Foerster
Conditional Fairness for Generative AIs
Chih-Hong Cheng, Harald Ruess, Changshun Wu, Xingyu Zhao
To what extent is ChatGPT useful for language teacher lesson plan creation?
Alex Dornburg, Kristin Davin
Fake Artificial Intelligence Generated Contents (FAIGC): A Survey of Theories, Detection Methods, and Opportunities
Xiaomin Yu, Yezhaohui Wang, Yanfang Chen, Zhen Tao, Dinghao Xi, Shichao Song, Simin Niu, Zhiyu Li
Enhancing Deep Knowledge Tracing via Diffusion Models for Personalized Adaptive Learning
Ming Kuo, Shouvon Sarker, Lijun Qian, Yujian Fu, Xiangfang Li, Xishuang Dong
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, Bo Luo
U Can't Gen This? A Survey of Intellectual Property Protection Methods for Data in Generative AI
Tanja Šarčević, Alicja Karlowicz, Rudolf Mayer, Ricardo Baeza-Yates, Andreas Rauber
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation
Jessica Quaye, Alicia Parrish, Oana Inel, Charvi Rastogi, Hannah Rose Kirk, Minsuk Kahng, Erin van Liemt, Max Bartolo, Jess Tsang, Justin White, Nathan Clement, Rafael Mosquera, Juan Ciro, Vijay Janapa Reddi, Lora Aroyo
Towards Next-Level Post-Training Quantization of Hyper-Scale Transformers
Junhan Kim, Chungman Lee, Eulrang Cho, Kyungphil Park, Ho-young Kim, Joonyoung Kim, Yongkweon Jeon