IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
arXiv preprint arXiv:2410.16531 , year=
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Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.
citing papers explorer
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Item Response Scaling Laws: A Measurement Theory Approach for Efficient and Generalizable Neural Scaling Estimation
IRSL applies IRT to reduce scaling law estimation from O(M×N) to O(M+N) parameters, enabling reliable estimates with only 50 questions per benchmark after calibration and generalizable ability scores across related benchmarks.
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Manifold Steering Reveals the Shared Geometry of Neural Network Representation and Behavior
Manifold steering along activation geometry induces behavioral trajectories matching the natural manifold of outputs, while linear steering produces off-manifold unnatural behaviors.
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Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space
LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.