From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models

arXiv:2604.25167v1 Announce Type: new Abstract: While mechanistic interpretability tools like Sparse Autoencoders (SAEs) can uncover meaningful features within Large Language Models (LLMs), a critical gap remains in transforming these insights into practical actions for model optimization. We bridge this gap with the hypothesis that data selection guided by a model's internal task features is a effective training strategy. Inspired by this, we propose Interpretability-Guided Data Selection (IGDS