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Concept Heterogeneity-aware Representation Steering
arXiv – CS AI|Laziz U. Abdullaev, Noelle Y. L. Wong, Ryan T. Z. Lee, Shiqi Jiang, Khoi N. M. Nguyen, Tan M. Nguyen||3 views
🤖AI Summary
Researchers introduce CHaRS (Concept Heterogeneity-aware Representation Steering), a new method for controlling large language model behavior that uses optimal transport theory to create context-dependent steering rather than global directions. The approach models representations as Gaussian mixture models and derives input-dependent steering maps, showing improved behavioral control over existing methods.
Key Takeaways
- →CHaRS addresses limitations of current LLM steering methods that assume homogeneous representation across embedding spaces.
- →The method uses optimal transport theory to model source and target representations as Gaussian mixture models.
- →Input-dependent steering maps are derived through barycentric projection, creating smooth kernel-weighted combinations of cluster-level shifts.
- →Experimental results demonstrate CHaRS provides more effective behavioral control than global steering approaches.
- →The research advances techniques for fine-grained control of large language model behavior at inference time.
#llm#representation-steering#optimal-transport#machine-learning#ai-control#inference#behavioral-control#gaussian-mixture-models
Read Original →via arXiv – CS AI
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