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About this paper
Zhongqiu Li, Shiquan Wang, Ruiyu Fang, Mengjiao Bao, Zhenhe Wu, Shuangyong Song, Yongxiang Li, Zhongjiang He
MR-UIE from TeleAI and BeiHang University enhances Universal Information Extraction (UIE) by training Large Language Models to employ multi-perspective reasoning, achieving new state-of-the-art results across multiple supervised and zero-shot benchmarks. It enables LLMs to dynamically select and execute optimal reasoning strategies for complex structured output scenarios.
Key insights
- Explicitly modeling reasoning as a structured, multi-path decision problem significantly improves LLMs' ability to handle complex and ambiguous IE tasks.
- A unified, task-agnostic schema representation is essential for enabling strong generalization to unseen schemas and ensuring robust output format compliance.
- Reinforcement learning can effectively internalize diverse reasoning strategies, allowing LLMs to adaptively construct contextually relevant inference paths for superior performance.
Summary by alphaXiv.
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