EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
Interpretation of Prediction Models Using the Input Gradient
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
State of the art machine learning algorithms are highly optimized to provide the optimal prediction possible, naturally resulting in complex models. While these models often outperform simpler more interpretable models by order of magnitudes, in terms of understanding the way the model functions, we are often facing a "black box". In this paper we suggest a simple method to interpret the behavior of any predictive model, both for regression and classification. Given a particular model, the information required to interpret it can be obtained by studying the partial derivatives of the model with respect to the input. We exemplify this insight by interpreting convolutional and multi-layer neural networks in the field of natural language processing.
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FaVeX accelerates verified explanations for neural networks via dynamic batch-sequential processing and query reuse while introducing verifier-optimal robust explanations that incorporate verifier incompleteness.
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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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Faster Verified Explanations for Neural Networks
FaVeX accelerates verified explanations for neural networks via dynamic batch-sequential processing and query reuse while introducing verifier-optimal robust explanations that incorporate verifier incompleteness.