An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications , shorttitle =
4 Pith papers cite this work. Polarity classification is still indexing.
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XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
Symb-xMIL is a post-hoc explanation framework that quantifies MIL model alignment with logical decision rules in histopathology to enable rule-based interpretability.
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.
citing papers explorer
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Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs)
An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
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XtrAIn: Training-Guided Occlusion for Feature Attribution
XtrAIn shifts occlusion from input space to parameter space along the training trajectory to produce cleaner feature attributions than standard methods.
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Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology
Symb-xMIL is a post-hoc explanation framework that quantifies MIL model alignment with logical decision rules in histopathology to enable rule-based interpretability.
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Correcting Influence: Unboxing LLM Outputs with Orthogonal Latent Spaces
A latent mediation framework with sparse autoencoders enables non-additive token-level influence attribution in LLMs by learning orthogonal features and back-propagating attributions.