ArXiv paper proposing a framework that uses interpretability analysis to guide training data selection for large language models. The approach aims to optimize model quality while reducing training costs by identifying which examples contribute most to model behavior.
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From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Researchers propose using interpretability analysis to identify which training examples most influence LLM behavior, cutting training costs while maintaining model quality.
Thursday, April 30, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline
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