Comprehensive Survey
Comprehensive surveys in various scientific fields systematically review existing research, aiming to synthesize key findings, identify gaps, and guide future directions. Current research focuses on evaluating and improving the trustworthiness, efficiency, and bias mitigation of models across diverse domains, including large language models, image generation, and autonomous systems. These surveys are crucial for advancing understanding within specific subfields and facilitating the development of more robust and reliable technologies with broader practical applications.
Papers
A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha
AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey
Yixuan Wu, Kaiyuan Hu, Danny Z. Chen, Jian Wu
Visual Text Meets Low-level Vision: A Comprehensive Survey on Visual Text Processing
Yan Shu, Weichao Zeng, Zhenhang Li, Fangmin Zhao, Yu Zhou
Toward Green and Human-Like Artificial Intelligence: A Complete Survey on Contemporary Few-Shot Learning Approaches
Georgios Tsoumplekas, Vladislav Li, Vasileios Argyriou, Anastasios Lytos, Eleftherios Fountoukidis, Sotirios K. Goudos, Ioannis D. Moscholios, Panagiotis Sarigiannidis