Papers by Usha Bhalla
Evaluating Adversarial Robustness of Concept Representations in Sparse Autoencoders (2026.eacl-long)
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| Challenge: | Existing evaluations of SAEs focus on metrics such as reconstruction-sparsity tradeoff, human (auto-)interpretability, and feature disentanglement, but they neglect robustness of concept representations to input perturbations. |
| Approach: | They propose an unsupervised approach to map LLM embeddings to sparse interpretable concept embeddables via dictionary learning. |
| Outcome: | The proposed framework shows that sparse autoencoders can manipulate concept-based interpretations without denoising or postprocessing. |