Comprehensive Benchmark
Comprehensive benchmarks are crucial for evaluating the performance and limitations of various machine learning models, particularly in specialized domains like computer vision, natural language processing, and graph learning. Recent research focuses on developing standardized evaluation frameworks that address issues like inconsistent experimental setups, limited task diversity, and the lack of robust metrics, encompassing diverse model architectures and algorithms. These benchmarks facilitate fair comparisons, identify areas for improvement in existing models, and ultimately accelerate progress in the field by providing a common ground for researchers to evaluate and compare their work. The resulting insights are vital for both advancing fundamental understanding and improving the reliability and trustworthiness of AI systems in real-world applications.
Papers
GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Pengfei Zhou, Xiaopeng Peng, Jiajun Song, Chuanhao Li, Zhaopan Xu, Yue Yang, Ziyao Guo, Hao Zhang, Yuqi Lin, Yefei He, Lirui Zhao, Shuo Liu, Tianhua Li, Yuxuan Xie, Xiaojun Chang, Yu Qiao, Wenqi Shao, Kaipeng Zhang
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Chen Zhou, Peng Cheng, Junfeng Fang, Yifan Zhang, Yibo Yan, Xiaojun Jia, Yanyan Xu, Kun Wang, Xiaochun Cao
FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness
Lucas Rosenblatt, R. Teal Witter
SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks
Tianhao Li, Jingyu Lu, Chuangxin Chu, Tianyu Zeng, Yujia Zheng, Mei Li, Haotian Huang, Bin Wu, Zuoxian Liu, Kai Ma, Xuejing Yuan, Xingkai Wang, Keyan Ding, Huajun Chen, Qiang Zhang