DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
This addresses the problem of systematic errors in text classification for ML practitioners, offering an incremental improvement over existing bias identification methods.
The paper tackles systematic biases in text classifiers by introducing DISCERN, a framework that generates natural language explanations of errors, which then improves classifier performance on three datasets and enhances human interpretation by over 25% relative efficiency.
Despite their high predictive accuracies, current machine learning systems often exhibit systematic biases stemming from annotation artifacts or insufficient support for certain classes in the dataset. Recent work proposes automatic methods for identifying and explaining systematic biases using keywords. We introduce DISCERN, a framework for interpreting systematic biases in text classifiers using language explanations. DISCERN iteratively generates precise natural language descriptions of systematic errors by employing an interactive loop between two large language models. Finally, we use the descriptions to improve classifiers by augmenting classifier training sets with synthetically generated instances or annotated examples via active learning. On three text-classification datasets, we demonstrate that language explanations from our framework induce consistent performance improvements that go beyond what is achievable with exemplars of systematic bias. Finally, in human evaluations, we show that users can interpret systematic biases more effectively (by over 25% relative) and efficiently when described through language explanations as opposed to cluster exemplars.