Continuous Interpretive Steering and the GraSD dataset reveal that LLMs encode graded sensitivity to scalar diversity in their internal representations, recoverable via controlled activation interventions.
InProceedings of the 62nd annual meet- ing of the association for computational linguistics (volume 1: Long papers), pages 15789–15809
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The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.
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Continuous Interpretive Steering for Scalar Diversity
Continuous Interpretive Steering and the GraSD dataset reveal that LLMs encode graded sensitivity to scalar diversity in their internal representations, recoverable via controlled activation interventions.
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The Master Key Hypothesis: Unlocking Cross-Model Capability Transfer via Linear Subspace Alignment
The Master Key Hypothesis states that capabilities are low-dimensional directions transferable across models through linear subspace alignment, with UNLOCK demonstrating gains such as 12.1% accuracy improvement on MATH when transferring CoT from 14B to 7B models.
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Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
The survey organizes mechanistic interpretability techniques into a Locate-Steer-Improve framework to enable actionable improvements in LLM alignment, capability, and efficiency.