← Search

Valérie Costa

2 accepted papers

2026

Priors in time: Missing inductive biases for language model interpretability

ICLR 2026poster

A central aim of interpretability tools applied to language models is to recover meaningful concepts from model activations. Existing feature extraction methods focus on single activations regardless of the context, implicitly assuming independence (and therefore stationarity). This leaves open whet…

Cited by 0SourcecodeScholar
2025

From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit

NeurIPS 2025poster

Motivated by the hypothesis that neural network representations encode abstract, interpretable features as linearly accessible, approximately orthogonal directions, sparse autoencoders (SAEs) have become a popular tool in interpretability literature. However, recent work has demonstrated phenomenolo…

Cited by 0SourceScholar