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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.618.pdf