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cs.LG2026
Learning to Be Monitored: Causal Bounds and Intervention-Aware Post-Training Recipes for Chain-of-Thought Monitorability
Qiang Fu, Xinyue Li, Andre Wibisono +1
Chain-of-thought (CoT) monitoring aims to detect potentially harmful behaviors in large language models (LLMs) from their CoTs. Intervention-based evaluations measure this property…
cs.LG2025
One-shot Optimized Steering Vectors Mediate Safety-relevant Behaviors in LLMs
Jacob Dunefsky, Arman Cohan
Steering vectors (SVs) have emerged as a promising approach for interpreting and controlling LLMs, but current methods typically require large contrastive datasets that are often i…
cs.LG2023
Observable Propagation: Uncovering Feature Vectors in Transformers
Jacob Dunefsky, Arman Cohan
A key goal of current mechanistic interpretability research in NLP is to find linear features (also called "feature vectors") for transformers: directions in activation space corre…