ProtoLens: Advancing Prototype Learning for Fine-Grained Interpretability in Text Classification
Year: 2025
Type: conference-paper
Abstract: 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.
Authors: Bowen Wei, Ziwei Zhu
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412886704