Benchmark Platform
Benchmark platforms in various scientific domains aim to provide standardized evaluations of models and algorithms, enabling fair comparisons and driving research progress. Current research focuses on developing comprehensive benchmarks across diverse areas, including natural language processing, computer vision, robotics, and healthcare, often incorporating novel model architectures like large language models and deep learning frameworks. These platforms are crucial for advancing the field by facilitating reproducible research, identifying limitations of existing methods, and ultimately leading to more robust and reliable systems with real-world applications. The resulting insights inform the development of improved algorithms and contribute to a more rigorous and transparent scientific process.
Papers
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
Swapnil Bhosale, Abhra Chaudhuri, Alex Lee Robert Williams, Divyank Tiwari, Anjan Dutta, Xiatian Zhu, Pushpak Bhattacharyya, Diptesh Kanojia
Benchmarking and In-depth Performance Study of Large Language Models on Habana Gaudi Processors
Chengming Zhang, Baixi Sun, Xiaodong Yu, Zhen Xie, Weijian Zheng, Kamil Iskra, Pete Beckman, Dingwen Tao
G4SATBench: Benchmarking and Advancing SAT Solving with Graph Neural Networks
Zhaoyu Li, Jinpei Guo, Xujie Si
Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
Qiuyu Wang, Zifan Shi, Kecheng Zheng, Yinghao Xu, Sida Peng, Yujun Shen
A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
Shaohui Mei, Jiawei Lian, Xiaofei Wang, Yuru Su, Mingyang Ma, Lap-Pui Chau