Papers
1–4 of 4ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations
Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predictions, but require ...
DLM-Scope: Mechanistic Interpretability of Diffusion Language Models via Sparse Autoencoders
Sparse autoencoders (SAEs) have become a standard tool for mechanistic interpretability in autoregressive large language models (LLMs), enabling researchers to extract sparse, human-interpretable feat...
Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
As large language models are increasingly trained and fine-tuned, practitioners need methods to identify which training data drive specific behaviors, particularly unintended ones. Training Data Attri...
The Confidence Manifold: Geometric Structure of Correctness Representations in Language Models
When a language model asserts that "the capital of Australia is Sydney," does it know this is wrong? We characterize the geometry of correctness representations across 9 models from 5 architecture fam...