Sign-Aware Gated Sparse Autoencoders: Modeling Anticorrelated Features with Bi-Jump-ReLU Activations
arXiv:2605.28149v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) extract interpretable features from Large Language Models, but standard variants enforce non-negativity, forcing separate latents for diametrically opposed concepts (e.g., "pressure too high" vs. "pressure too low") and wasting dictionary capacity when features are anticorrelated. We propose the Sign-Aware Gated SAE (SA-GSAE): two-sided gated sparsity with signed magnitude and auxiliary supervision. A polarity-sensitive gate selects support on either sign, a signed-magnitude path avoids L1 shrinkage, and an auxiliary re
The continuous drive to improve the efficiency, interpretability, and capacity of large language models (LLMs) necessitates innovations in underlying architectural components like sparse autoencoders.
Improving the efficiency of feature extraction in LLMs directly enhances model performance, reduces computational costs, and enables more robust and interpretable AI systems, which is critical for future AI applications.
This research introduces a novel autoencoder architecture that can model anticorrelated features more effectively, potentially leading to more compact and powerful LLM representations than previously possible.
- · AI researchers
- · Large Language Model developers
- · Cloud AI providers
- · Data scientists
- · Developers relying on less efficient autoencoder architectures
- · Projects with high computational budgets for LLM training
More efficient and interpretable feature learning within AI models, particularly LLMs.
Reduced training times and inference costs for complex AI systems leveraging these improved autoencoders, lowering barriers to entry for advanced AI development.
Acceleration of AI research and deployment in fields requiring highly accurate and efficient understanding of nuanced, bidirectional concepts, potentially impacting areas like scientific discovery and advanced human-computer interaction.
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Read at arXiv cs.LG