MetaLead: A Comprehensive Human-Curated Leaderboard Dataset for Transparent Reporting of Machine Learning Experiments
Year: 2026
Type: conference-paper
Abstract: Leaderboards are crucial in the machine learning (ML) domain for benchmarking and tracking progress.However, creating leaderboards traditionally demands significant manual effort.In recent years, efforts have been made to automate leaderboard generation, but existing datasets for this purpose are limited by capturing only the best results from each paper and limited metadata.We present MET-ALEAD, a fully human-annotated ML leaderboard dataset that captures all experimental results for result transparency and contains extra metadata, such as the result experimental type(baseline, proposed method, or variation of proposed method) for experiment-type guided comparisons, and explicitly separates train and test dataset for cross-domain assessment.This enriched structure makes METALEAD a powerful resource for more transparent and nuanced evaluations across ML research.
Authors: Roelien C. Timmer, Necva Bölücü, Stephen Wan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7140144222