From Confusion to Clarity: Confusion-Aware Retrieval and Knowledge Injection for Text Classification
A framework is proposed that identifies which label pairs the model struggles to distinguish, expands the candidate set to include confusable labels, and generates targeted rules to differentiate between similar candidates, which requires no fine-tuning and transfers to smaller, cheaper models.
Manish Gupta, Chaitanya Giri, Jayasimha Talur
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