LA-RL: Label-Aware Self-Reflection for Reinforcement Learning in Information Extraction
Quick Answer
LA-RL introduces a label-aware self-reflection framework for information extraction, enhancing performance in tasks like named entity recognition and relation extraction.
Quick Take
The model achieves an average F1 score of 6.83 on SciER relation extraction and shows significant improvements over standard fine-tuning methods, particularly in out-of-distribution scenarios.
Key Points
- LA-RL uses task-grounded diagnostic labels for self-correction in information extraction.
- Achieved 6.83 average F1 score on SciER relation extraction benchmarks.
- Significant gains observed in out-of-distribution relation extraction tasks.
- Training involves cold-start supervised fine-tuning and two stages.
- Reflection structure is task-sensitive, benefiting relation extraction more than named entity recognition.
Paper Resources
Source Excerpt
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-correct
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