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Current approaches for fixing systematic problems in NLP models (e.g. regexpatches, finetuning on more data) are either brittle, or labor-intensive andliable to shortcuts. In contrast, humans often provide corrections to eachother through natural language. Taking inspiration from this, we explorenatural language patches -- declarative statements that allow developers toprovide corrective feedback at the right level of abstraction, eitheroverriding the model (``if a review gives 2 stars, the sentiment is negative'')or providing additional information the model may lack (``if something isdescribed as the bomb, then it is good''). We model the task of determining ifa patch applies separately from the task of integrating patch information, andshow that with a small amount of synthetic data, we can teach models toeffectively 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, andF1 by 7 points on a relation extraction dataset. Finally, we show thatfinetuning on as many as 100 labeled examples may be needed to match theperformance of a small set of language patches.

Fixing Model Bugs with Natural Language Patches