Generative Artificial Intelligence
Generative Artificial Intelligence (GenAI) focuses on creating new data samples—text, images, code, etc.—from existing datasets using deep learning models. Current research emphasizes diverse applications, including drug discovery, education, and industrial processes, with a focus on model architectures like transformers, diffusion models, and generative adversarial networks (GANs). The field's significance lies in its potential to automate complex tasks, accelerate scientific discovery, and reshape various industries, while also raising important ethical considerations regarding bias, data privacy, and the responsible use of AI.
Papers
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity
Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu
Search Still Matters: Information Retrieval in the Era of Generative AI
William R. Hersh
Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle
Lixiang Yan, Roberto Martinez-Maldonado, Dragan Gašević
Comparative Experimentation of Accuracy Metrics in Automated Medical Reporting: The Case of Otitis Consultations
Wouter Faber, Renske Eline Bootsma, Tom Huibers, Sandra van Dulmen, Sjaak Brinkkemper
The Rise of Creative Machines: Exploring the Impact of Generative AI
Saad Shaikh, Rajat bendre, Sakshi Mhaske