Papers by Qinqin He

1 papers
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework (2026.findings-acl)

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Challenge: Existing studies on how SAEs derive most fine-grained latent features for safety remain unexplored.
Approach: They propose a framework for interpreting SAE features in safety-critical domains . they train a suite of SAEs with human-readable explanations and systematic evaluations based on pornography, politics, violence, and terror .
Outcome: The proposed framework reduces interpretation cost by 55% and improves safety-critical features.

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