用强化学习让大模型主动推理，提升信息抽取准确率。
MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction
- 引入强化学习使模型从被动提取转为主动推理
- 在多个数据集上准确率超越当前最优方法
- 适合需要多步推理的复杂信息抽取任务
大型语言模型在多个研究领域展现出强大能力，但在通用信息抽取（UIE）任务中表现仍不足，尤其在涉及复杂模式描述和多步推理的结构化输出场景下。现有方法通过上下文学习和指令微调提升性能，但仍存在显著局限。为此，我们提出将强化学习与多视角推理结合，用于信息抽取任务。该方法使大模型从被动抽取者转变为积极推理者，不仅理解提取内容，还掌握推理过程。在多个信息抽取基准上的实验表明，MR-UIE在跨领域任务中持续提升抽取准确率，在多个数据集上优于现有先进方法。此外，将多视角推理融入强化学习显著增强了复杂任务中的泛化能力，凸显了推理机制在挑战性场景中的关键作用。
原文摘要 · Abstract (English)
Large language models (LLMs) demonstrate robust capabilities across diverse research domains. However, their performance in universal information extraction (UIE) remains insufficient, especially when tackling structured output scenarios that involve complex schema descriptions and require multi-step reasoning. While existing approaches enhance the performance of LLMs through in-context learning and instruction tuning, significant limitations nonetheless persist. To enhance the model's generalization ability, we propose integrating reinforcement learning (RL) with multi-perspective reasoning for information extraction (IE) tasks. Our work transitions LLMs from passive extractors to active reasoners, enabling them to understand not only what to extract but also how to reason. Experiments conducted on multiple IE benchmarks demonstrate that MR-UIE consistently elevates extraction accuracy across domains and surpasses state-of-the-art methods on several datasets. Furthermore, incorporating multi-perspective reasoning into RL notably enhances generalization in complex IE tasks, underscoring the critical role of reasoning in challenging scenarios.
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