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GREYC at CRF Filling 2026: Rewrite Before You Extract - Rewriting Clinical Notes for Automated CRF

This paper describes the system we submitted to the CRF:filling 2026 shared task. We propose a modular, LLM-based framework including an LLM as rewriter, which enhances the original clinical note from the perspective of each target CRF item; an LLM extractor, which retrieves the relevant value using a k-shot prompting strategy; and an LLM as a judge, which determines whether the clinical note contains evidence to support a given answer, defaulting to ’unknown’ otherwise. We evaluated our system on the English portion of the dataset; our complete framework achieves a macro-F1 of 0.64 on the development set. Our analysis reveals that while the rewriting step effectively generates correct factual information, it also increases false positives. The judge component mitigates this by adopting a conservative prediction strategy that substantially reduces false positives at the cost of a moderate reduction in true positives, yielding higher precision and better alignment with the shared task metric. On the test set, a light version of our system ranked 21 out of 32 public submissions, achieving a macro-F1 of 0.45.