LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction
Large language models show strong promise for information extraction (IE), but existing reflection-based correction methods are often misaligned with structured extraction outputs. Free-form self-reflection can flag an error, yet it rarely identifies whether the failure is a missing span, wrong label, boundary mismatch, invalid relation type, or reversed argument order. We introduce LA-RL (Label-Aware Reflective Reinforcement Learning), an outcome-supervised framework that guides IE self-correction with task-grounded diagnostic labels. A single backbone first predicts an extraction, diagnoses task-specific error labels, and then revises its output conditioned on the diagnosis. Training st...
| Authors | Xiao You, Tianwei Yan, Zixu Shan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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