aiqbench analysis
This paper introduces a rigorous post-hoc certification framework to quantify when Sparse Autoencoder (SAE) reconstructions reliably preserve the predictive behavior of frozen language models. By deriving bounds based on proxy risk, reconstruction loss gaps, and support mismatch, the authors provide a practical method for validating SAE-based interpretability without altering the original model weights. The study empirically demonstrates that later layers in larger models like Llama-3-8B are generally easier to certify due to higher reconstruction fidelity, offering clear guidelines for researchers relying on SAEs to analyze internal representations.
When Can We Trust the Sparse Lens? A Certification Framework for SAE Faithfulness
arXiv:2606.18383v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are increasingly used to study internal representations in language models (LMs), but it remains unclear when an SAE-based representation reliably preserves the underlying model's predictive behavior. We introduce a post-hoc certification framework for this purpose. At a chosen layer, we replace the model's hidden activation with its pretrained SAE reconstruction and derive a bound on the expected loss of the original frozen LM through this SAE-based proxy. The resulting certificate depends on three quantities measured on held-out data: the proxy risk, the reconstruction loss gap, and support mismatch between the calibration feature pool and new inputs. We further derive a more conservative exact-$P$ extension whose proxy-risk concentration term is uniform over all feature pools of the same size. Empirically, the certificates become non-vacuous for GPT-2 Small, Gemma-2B, and Llama-3-8B. A detailed layerwise study of Llama-3-8B shows that later layers are easier to certify, driven mainly by improved reconstruction fidelity and weaker downstream amplification of reconstruction error. GPT-2 Small shows much weaker layer dependence, indicating that this pattern is not universal across model-SAE pairs. Overall, the framework provides a practical way to determine whether an SAE representation preserves enough of a frozen LM's predictive behavior to support certification of the original model. Code is available at: https://github.com/newcodevelop/sparse-lens-certification.
Title:When Can We Trust the Sparse Lens? A Certification Framework for SAE Faithfulness
Abstract:Sparse autoencoders (SAEs) are increasingly used to study internal representations in language models (LMs), but it remains unclear when an SAE-based representation reliably preserves the underlying model's predictive behavior. We introduce a post-hoc certification framework for this purpose. At a chosen layer, we replace the model's hidden activation with its pretrained SAE reconstruction and derive a bound on the expected loss of the original frozen LM through this SAE-based proxy. The resulting certificate depends on three quantities measured on held-out data: the proxy risk, the reconstruction loss gap, and support mismatch between the calibration feature pool and new inputs. We further derive a more conservative exact-$P$ extension whose proxy-risk concentration term is uniform over all feature pools of the same size. Empirically, the certificates become non-vacuous for GPT-2 Small, Gemma-2B, and Llama-3-8B. A detailed layerwise study of Llama-3-8B shows that later layers are easier to certify, driven mainly by improved reconstruction fidelity and weaker downstream amplification of reconstruction error. GPT-2 Small shows much weaker layer dependence, indicating that this pattern is not universal across model-SAE pairs. Overall, the framework provides a practical way to determine whether an SAE representation preserves enough of a frozen LM's predictive behavior to support certification of the original model. Code is available at: this https URL.
Accepted at Transactions of the Association for Computational Linguistics (TACL). Pre-MIT Press publication version
Machine Learning (cs.LG); Computation and Language (cs.CL)
arXiv:2606.18383 [cs.LG]
(or arXiv:2606.18383v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.18383 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 334 words
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