llm-control

This paper investigates when activation steering succeeds or fails for LLMs by analyzing early decoding dynamics. The authors introduce ASTEER, a large testbed of steered generations, and train a GBDT classifier to predict steering outcomes from early hidden states, enabling efficient steering strength search.

This paper investigates when rank-1 activation steering is effective and cost-efficient, proposing geometry-guided search and the concept of granularity to explain variability, and introduces the GRACE framework for efficient LLM control.