Papers with sparse linear combination of SAE features

1 papers
Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders (2025.acl-long)

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Challenge: Large Language Models (LLMs) exhibit impressive abilities in various domains such as text generation, instruction following, and reasoning.
Approach: They propose a method to decompose the activations of Large Language Models into a sparse linear combination of SAE features.
Outcome: The proposed method shows that some features are strongly related to specific languages, while others are unaffected by ablating them.

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