Stop Measuring Calibration When Humans Disagree

Joris Baan, Wilker Aziz, Barbara Plank, Raquel Fernandez


Abstract
Calibration is a popular framework to evaluate whether a classifier knows when it does not know - i.e., its predictive probabilities are a good indication of how likely a prediction is to be correct. Correctness is commonly estimated against the human majority class. Recently, calibration to human majority has been measured on tasks where humans inherently disagree about which class applies. We show that measuring calibration to human majority given inherent disagreements is theoretically problematic, demonstrate this empirically on the ChaosNLI dataset, and derive several instance-level measures of calibration that capture key statistical properties of human judgements - including class frequency, ranking and entropy.
Anthology ID:
2022.emnlp-main.124
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1892–1915
Language:
URL:
https://aclanthology.org/2022.emnlp-main.124
DOI:
Bibkey:
Cite (ACL):
Joris Baan, Wilker Aziz, Barbara Plank, and Raquel Fernandez. 2022. Stop Measuring Calibration When Humans Disagree. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 1892–1915, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Stop Measuring Calibration When Humans Disagree (Baan et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.124.pdf