Detecting Label Errors by Using Pre-Trained Language Models

Derek Chong, Jenny Hong, Christopher Manning


Abstract
We show that large pre-trained language models are inherently highly capable of identifying label errors in natural language datasets: simply examining out-of-sample data points in descending order of fine-tuned task loss significantly outperforms more complex error-detection mechanisms proposed in previous work. To this end, we contribute a novel method for introducing realistic, human-originated label noise into existing crowdsourced datasets such as SNLI and TweetNLP. We show that this noise has similar properties to real, hand-verified label errors, and is harder to detect than existing synthetic noise, creating challenges for model robustness.We argue that human-originated noise is a better standard for evaluation than synthetic noise. Finally, we use crowdsourced verification to evaluate the detection of real errors on IMDB, Amazon Reviews, and Recon, and confirm that pre-trained models perform at a 9–36% higher absolute Area Under the Precision-Recall Curve than existing models.
Anthology ID:
2022.emnlp-main.618
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9074–9091
Language:
URL:
https://aclanthology.org/2022.emnlp-main.618
DOI:
10.18653/v1/2022.emnlp-main.618
Bibkey:
Cite (ACL):
Derek Chong, Jenny Hong, and Christopher Manning. 2022. Detecting Label Errors by Using Pre-Trained Language Models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 9074–9091, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Detecting Label Errors by Using Pre-Trained Language Models (Chong et al., EMNLP 2022)
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PDF:
https://aclanthology.org/2022.emnlp-main.618.pdf