Atlas-Alignment: Making Interpretability Transferable Across Language Models
Interpreting what large language models “think” is slow and expensive. Each new model often needs custom tools and lots of manual labeling.
Atlas-Alignment offers a shortcut. Instead of rebuilding everything, it lines up a new model’s hidden activity with a shared, human-labeled Concept Atlas using only overlapping inputs and lightweight alignment—no new concept labels required.
- Find features: Search for meaningful, human concepts inside an unfamiliar model.
- Steer behavior: Nudge generation toward or away from selected concepts.
The payoff: invest once in a high-quality Concept Atlas, then make many future models more transparent and controllable at minimal marginal cost—helping scale explainable, reliable AI.
By Bruno Puri, Jim Berend, Sebastian Lapuschkin, and Wojciech Samek. Read the paper: http://arxiv.org/abs/2510.27413v1
Paper: http://arxiv.org/abs/2510.27413v1
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