Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?
Pedro Rodriguez, Joe Barrow, Alexander Miserlis Hoyle, John P. Lalor, Robin Jia, Jordan Boyd-Graber
Abstract
Leaderboards are widely used in NLP and push the field forward. While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models). Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made. Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses. Using this model, we analyze the ranking reliability of leaderboards. Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples. We conclude with recommendations for future benchmark tasks.
BibTeX
@inproceedings{rodriguez-etal-2021-evaluation,
title = "Evaluation Examples are not Equally Informative: How should that change {NLP} Leaderboards?",
author = "Rodriguez, Pedro and
Barrow, Joe and
Hoyle, Alexander Miserlis and
Lalor, John P. and
Jia, Robin and
Boyd-Graber, Jordan",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.346/",
doi = "10.18653/v1/2021.acl-long.346",
pages = "4486--4503"
}