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Found 4 papers, 1 papers with code
Towards Safe Large Language Models for Medicine
As large language models (LLMs) develop ever-improving capabilities and are applied in real-world settings, it is important to understand their safety.
Efficient Estimation of Average-Case Robustness for Multi-Class Classification
These estimators linearize models in the local region around an input and analytically compute the robustness of the resulting linear models.
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations
By bringing diverse explanation methods into a common framework, this work (1) advances the conceptual understanding of these methods, revealing their shared local function approximation objective, properties, and relation to one another, and (2) guides the use of these methods in practice, providing a principled approach to choose among methods and paving the way for the creation of new ones.
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
To this end, we first conduct interviews with data scientists to understand what constitutes disagreement between explanations generated by different methods for the same model prediction, and introduce a novel quantitative framework to formalize this understanding.
