AlignSAE: Concept-Aligned Sparse Autoencoders
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909988353474560 |
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| author | Yang, Minglai Guo, Xinyu Shi, Zhengliang Bi, Jinhe Bethard, Steven Surdeanu, Mihai Pan, Liangming |
| author_facet | Yang, Minglai Guo, Xinyu Shi, Zhengliang Bi, Jinhe Bethard, Steven Surdeanu, Mihai Pan, Liangming |
| contents | Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse Autoencoders (SAEs) can decompose hidden activations into more fine-grained, interpretable features, they often struggle to reliably align these features with human-defined concepts, resulting in entangled and distributed feature representations. To address this, we introduce AlignSAE, a method that aligns SAE features with a predefined ontology through a "pre-train, then post-train" curriculum. After an initial unsupervised training phase, we apply supervised post-training to bind specific concepts to dedicated latent slots while preserving the remaining capacity for general reconstruction. This separation creates an interpretable interface where specific concepts can be inspected and controlled without interference from unrelated features. Empirical results demonstrate that AlignSAE enables precise causal interventions, such as reliable "concept swaps", by targeting single, semantically aligned slots, and further supports multi-hop reasoning and a mechanistic probe of grokking-like generalization dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AlignSAE: Concept-Aligned Sparse Autoencoders Yang, Minglai Guo, Xinyu Shi, Zhengliang Bi, Jinhe Bethard, Steven Surdeanu, Mihai Pan, Liangming Machine Learning Computation and Language Large Language Models (LLMs) encode factual knowledge within hidden parametric spaces that are difficult to inspect or control. While Sparse Autoencoders (SAEs) can decompose hidden activations into more fine-grained, interpretable features, they often struggle to reliably align these features with human-defined concepts, resulting in entangled and distributed feature representations. To address this, we introduce AlignSAE, a method that aligns SAE features with a predefined ontology through a "pre-train, then post-train" curriculum. After an initial unsupervised training phase, we apply supervised post-training to bind specific concepts to dedicated latent slots while preserving the remaining capacity for general reconstruction. This separation creates an interpretable interface where specific concepts can be inspected and controlled without interference from unrelated features. Empirical results demonstrate that AlignSAE enables precise causal interventions, such as reliable "concept swaps", by targeting single, semantically aligned slots, and further supports multi-hop reasoning and a mechanistic probe of grokking-like generalization dynamics. |
| title | AlignSAE: Concept-Aligned Sparse Autoencoders |
| topic | Machine Learning Computation and Language |
| url | https://arxiv.org/abs/2512.02004 |