MASE: Interpretable NLP Models via Model-Agnostic Saliency Estimation
Published:Dec 4, 2025 02:20
•1 min read
•ArXiv
Analysis
This article introduces MASE, a method for creating interpretable NLP models. The focus is on model-agnostic saliency estimation, suggesting a broad applicability across different NLP architectures. The title clearly states the core contribution: interpretability.
Key Takeaways
- •Focus on model interpretability in NLP.
- •Utilizes model-agnostic saliency estimation.
- •Suggests broad applicability across various NLP models.
Reference
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