Leaderboard Extraction

Leaderboard extraction involves automatically creating and maintaining rankings of AI models based on their performance across various tasks and datasets. Current research focuses on improving the accuracy and efficiency of this process, often leveraging large language models (LLMs) to extract relevant information from research papers and automatically generate (Task, Dataset, Metric, Score) quadruples. This automated approach addresses the growing challenge of manually tracking the rapidly evolving landscape of AI models, facilitating more efficient benchmarking and comparison within the scientific community and enabling more informed model selection for practical applications. Furthermore, research is exploring the robustness and limitations of existing leaderboards, aiming to develop more reliable and comprehensive evaluation methods.