By identifying confusable label pairs and generating targeted differentiation rules, you can improve text classification accuracy by up to 10 points without retraining—and these rules transfer to smaller, cheaper models.
This paper tackles a key challenge in text classification: when LLMs must choose between many similar category labels, they often get confused. The authors propose a system that identifies which label pairs the model struggles with, includes those confusing pairs in the candidate set, and generates targeted rules to help distinguish between them.