2 papers
cs.CL2026
Training Language Models to Explain Their Own Computations
Belinda Z. Li, Zifan Carl Guo, Vincent Huang +2
Can language models (LMs) learn to faithfully describe their internal computations? Are they better able to describe themselves than other models? We study the extent to which LMs'…
cs.AI2025
Predictive Concept Decoders: Training Scalable End-to-End Interpretability Assistants
Vincent Huang, Dami Choi, Daniel D. Johnson +2
Interpreting the internal activations of neural networks can produce more faithful explanations of their behavior, but is difficult due to the complex structure of activation space…