ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification
Bowen Wei, Ziwei Zhu · 2025
In this work, we propose ProtoLens, a novel prototype-based model that provides finegrained, sub-sentence level interpretability for text classification.ProtoLens uses a Prototypeaware Span Extraction module to identify relevant text spans associated with learned prototypes and a Prototype Alignment mechanism to ensure prototypes are semantically meaningful throughout training.By aligning the prototype embeddings with human-understandable examples, ProtoLens provides interpretable predictions while maintaining competitive accuracy.Extensive experiments demonstrate that ProtoLens outperforms both prototype-based and non-interpretable baselines on multiple text classification benchmarks.