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Found 12 papers, 4 papers with code
Self-AMPLIFY: Improving Small Language Models with Self Post Hoc Explanations
Incorporating natural language rationales in the prompt and In-Context Learning (ICL) has led to a significant improvement of Large Language Models (LLMs) performance.
Dynamic Interpretability for Model Comparison via Decision Rules
Explainable AI (XAI) methods have mostly been built to investigate and shed light on single machine learning models and are not designed to capture and explain differences between multiple models effectively.
Achieving Diversity in Counterfactual Explanations: a Review and Discussion
In the field of Explainable Artificial Intelligence (XAI), counterfactual examples explain to a user the predictions of a trained decision model by indicating the modifications to be made to the instance so as to change its associated prediction.
TIGTEC : Token Importance Guided TExt Counterfactuals
Counterfactual examples explain a prediction by highlighting changes of instance that flip the outcome of a classifier.
Integrating Prior Knowledge in Post-hoc Explanations
In the field of eXplainable Artificial Intelligence (XAI), post-hoc interpretability methods aim at explaining to a user the predictions of a trained decision model.
cgSpan: Pattern Mining in Conceptual Graphs
Conceptual Graphs (CGs) are a graph-based knowledge representation formalism.
Fuzzy Conceptual Graphs: a comparative discussion
Conceptual Graphs (CG) are a graph-based knowledge representation and reasoning formalism; fuzzy Conceptual Graphs (fCG) constitute an extension that enriches their expressiveness, exploiting the fuzzy set theory so as to relax their constraints at various levels.
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations
Post-hoc interpretability approaches have been proven to be powerful tools to generate explanations for the predictions made by a trained black-box model.
Issues with post-hoc counterfactual explanations: a discussion
Counterfactual post-hoc interpretability approaches have been proven to be useful tools to generate explanations for the predictions of a trained blackbox classifier.
Detecting Potential Local Adversarial Examples for Human-Interpretable Defense
Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval.
Defining Locality for Surrogates in Post-hoc Interpretablity
Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box.
Inverse Classification for Comparison-based Interpretability in Machine Learning
In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the test data).
