EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.