Papers by Harryn Oh
FaithfulSAE: Towards Capturing Faithful Features with Sparse Autoencoders without External Datasets Dependency (2025.acl-srw)
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| Challenge: | Sparse Autoencoders (SAEs) have emerged as a promising solution for decomposing large language model representations into interpretable features. |
| Approach: | They propose a method that trains SAEs on the model’s own synthetic dataset and a model-specific model to capture model-internal features. |
| Outcome: | The proposed method outperforms SAEs trained on web-based datasets and exhibits lower Fake Feature Ratio in 5 out of 7 models. |