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arXiv · 2211.03318

Fixing Model Bugs with Natural Language Patches

Shikhar Murty·Christopher D. Manning·Scott Lundberg·Marco Tulio Ribeiro
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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.

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Shikhar Murty, Christopher D. Manning, Scott Lundberg, Marco Tulio Ribeiro. 2022-11-07. Fixing Model Bugs with Natural Language Patches. https://arxiv.org/abs/2211.03318

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