Papers with black-box LMs

1 papers
GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification (2025.coling-main)

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Challenge: Existing models with black-box nature obscure decision-making process and lack interpretability.
Approach: They propose a multi-head graph attention-based prototypical network that uses a vector and prototypes to learn an interpretable prototypical representation.
Outcome: The proposed model achieves superior results without sacrificing the accuracy of the original black-box LMs.

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