3 papers
cs.CL2026
Learning Self-Interpretation from Interpretability Artifacts: Training Lightweight Adapters on Vector-Label Pairs
Keenan Pepper, Alex McKenzie, Florin Pop +6
Self-interpretation methods prompt language models to describe their own internal states, but remain unreliable due to hyperparameter sensitivity. We show that training lightweight…
cs.LG2024
Unexpected Benefits of Self-Modeling in Neural Systems
Vickram N. Premakumar, Michael Vaiana, Florin Pop +4
Self-models have been a topic of great interest for decades in studies of human cognition and more recently in machine learning. Yet what benefits do self-models confer? Here we sh…
cs.CL2024
Rethinking harmless refusals when fine-tuning foundation models
Florin Pop, Judd Rosenblatt, Diogo Schwerz de Lucena +1
In this paper, we investigate the degree to which fine-tuning in Large Language Models (LLMs) effectively mitigates versus merely conceals undesirable behavior. Through the lens of…