Ferenc Szigeti


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SHAP-Based Explanation Methods: A Review for NLP Interpretability
Edoardo Mosca | Ferenc Szigeti | Stella Tragianni | Daniel Gallagher | Georg Groh
Proceedings of the 29th International Conference on Computational Linguistics

Model explanations are crucial for the transparent, safe, and trustworthy deployment of machine learning models. The SHapley Additive exPlanations (SHAP) framework is considered by many to be a gold standard for local explanations thanks to its solid theoretical background and general applicability. In the years following its publication, several variants appeared in the literature—presenting adaptations in the core assumptions and target applications. In this work, we review all relevant SHAP-based interpretability approaches available to date and provide instructive examples as well as recommendations regarding their applicability to NLP use cases.