Imagine a world where keeping up with the latest breakthroughs in AI research isn't a tedious chore of sifting through endless papers. That's the promise of a new study exploring how to automatically generate AI leaderboards, those crucial rankings that tell us which models perform best on which tasks. Traditionally, these leaderboards have been painstakingly curated by hand, a slow and laborious process. This new research proposes a smarter way: using instruction fine-tuning with large language models (LLMs). Researchers took the powerful FLAN-T5 model and trained it on a massive dataset of AI papers, teaching it to extract key information like the task being performed, the dataset used, the metric for evaluation, and the final score. The result? An automated system that can generate leaderboards with impressive accuracy. The model excels at identifying whether a paper even contains leaderboard information and can extract the relevant details needed to build a comprehensive ranking. This isn't just about saving time and effort. This automated approach allows us to keep up with the rapid pace of AI advancements, providing a real-time view of the state-of-the-art. It also overcomes the limitations of previous methods that relied on predefined categories, opening the door to discovering new tasks, datasets, and metrics as they emerge. While the model shows great promise, it's not without its challenges. Extracting numerical scores remains a hurdle, and inconsistencies in crowdsourced data can sometimes throw the model off track. But as the technology evolves, it opens up a future where easily accessible and updated performance data can accelerate the progress of AI research.