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Learning to Generate and Extract: A Multi-Agent Collaboration Framework For Zero-shot Document-level Event Arguments Extraction

arXiv – CS AI|Guangjun Zhang, Hu Zhang, Yazhou Han, Yue Fan, Yuhang Shao, Ru Li, Hongye Tan|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce a multi-agent collaboration framework for zero-shot document-level event argument extraction that uses AI agents to generate, evaluate, and refine synthetic training data. The system employs reinforcement learning to iteratively improve both data generation quality and argument extraction performance through a collaborative process.

Key Takeaways

→New multi-agent AI framework addresses data scarcity in document-level event argument extraction through synthetic data generation.
→System uses two AI agents that collaborate in a propose-evaluate-revise cycle similar to human cognitive processes.
→Framework incorporates reinforcement learning with reward signals based on semantic consistency and event structure constraints.
→Method shows improvements in both data generation quality and extraction performance across multiple zero-shot scenarios.
→Generated synthetic data enhances performance of other document-level event argument extraction models.

#ai #machine-learning #multi-agent #zero-shot #data-extraction #reinforcement-learning #nlp #synthetic-data

Read Original →via arXiv – CS AI

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