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GLARE is an LLM-based interface that translates natural language questions into SQL queries over local explanation data, enabling users to interactively explore global explanations of black-box image classifiers.
This paper presents a framework for prototype-based explanations that integrates feature importance at local and global levels, using 'alike parts' to highlight relevant feature subsets and augmenting prototype selection with feature diversity, evaluated on tabular datasets.