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Found 6 papers, 5 papers with code
Attention Meets Post-hoc Interpretability: A Mathematical Perspective
Attention-based architectures, in particular transformers, are at the heart of a technological revolution.
Faithful and Robust Local Interpretability for Textual Predictions
Interpretability is essential for machine learning models to be trusted and deployed in critical domains.
Understanding Post-hoc Explainers: The Case of Anchors
In many scenarios, the interpretability of machine learning models is a highly required but difficult task.
Comparing Feature Importance and Rule Extraction for Interpretability on Text Data
Complex machine learning algorithms are used more and more often in critical tasks involving text data, leading to the development of interpretability methods.
A Sea of Words: An In-Depth Analysis of Anchors for Text Data
For text data, it proposes to explain a decision by highlighting a small set of words (an anchor) such that the model to explain has similar outputs when they are present in a document.
SMACE: A New Method for the Interpretability of Composite Decision Systems
To explain such decisions, we propose the Semi-Model-Agnostic Contextual Explainer (SMACE), a new interpretability method that combines a geometric approach for decision rules with existing interpretability methods for machine learning models to generate an intuitive feature ranking tailored to the end user.
