Fixing Model Bugs with Natural Language Patches

Shikhar Murty, Christopher Manning, Scott Lundberg, Marco Tulio Ribeiro

Abstract
Current approaches for fixing systematic problems in NLP models (e.g., regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In contrast, humans often provide corrections to each other through natural language. Taking inspiration from this, we explore natural language patches—declarative statements that allow developers to provide corrective feedback at the right level of abstraction, either overriding the model (“if a review gives 2 stars, the sentiment is negative”) or providing additional information the model may lack (“if something is described as the bomb, then it is good”). We model the task of determining if a patch applies separately from the task of integrating patch information, and show that with a small amount of synthetic data, we can teach models to effectively use real patches on real data—1 to 7 patches improve accuracy by ~1–4 accuracy points on different slices of a sentiment analysis dataset, and F1 by 7 points on a relation extraction dataset. Finally, we show that finetuning on as many as 100 labeled examples may be needed to match the performance of a small set of language patches.
Anthology ID:
2022.emnlp-main.797
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:
11600–11613
Language:
URL:
https://aclanthology.org/2022.emnlp-main.797
DOI:
10.18653/v1/2022.emnlp-main.797
Bibkey:
Cite (ACL):
Shikhar Murty, Christopher Manning, Scott Lundberg, and Marco Tulio Ribeiro. 2022. Fixing Model Bugs with Natural Language Patches. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11600–11613, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Fixing Model Bugs with Natural Language Patches (Murty et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.797.pdf
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