NLP

Evaluation Examples Are Not Equally Informative: How Should That Change NLP Leaderboards?

August 1, 2021

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.

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AUTHORS

Written by

Pedro Rodriguez

Joe Barrow

Alexander Hoyle

John P. Lalor

Robin Jia

Jordan Boyd-Graber

Publisher

ACL

Research Topics

Natural Language Processing

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