Abstract
Although Large language Model (LLM)-powered information extraction (IE) systems have shown impressive capabilities, current fine-tuning paradigms face two major limitations: high training costs and difficulties in aligning with LLM preferences. To address these issues, we propose a novel universal IE paradigm, the Self-Correcting Iterative Refinement (SCIR) framework, along with a Multi-task Bilingual (Chinese-English) Self-Correcting (MBSC) dataset containing over 100,000 entries. The SCIR framework achieves plug-and-play compatibility with existing LLMs and IE systems through its Dual-Path Self-Correcting module and feedback-driven optimization, thereby significantly reducing training costs. Concurrently, the MBSC dataset tackles the challenge of preference alignment by indirectly distilling GPT-4's capabilities into IE result detection models. Experimental results demonstrate that SCIR outperforms state-of-the-art IE methods across three key tasks: named entity recognition, relation extraction, and event extraction, achieving a 5.27 percent average improvement in span-based Micro-F1 while reducing training costs by 87 percent compared to baseline approaches. These advancements not only enhance the flexibility and accuracy of IE systems but also pave the way for lightweight and efficient IE paradigms.
Abstract (translated)
尽管大型语言模型（LLM）驱动的信息抽取（IE）系统展现了令人印象深刻的能力，但当前的微调范式面临着两个主要限制：高昂的训练成本和与LLM偏好对齐的困难。为了解决这些问题，我们提出了一种新型的通用信息抽取框架——自我校正迭代精炼（Self-Correcting Iterative Refinement, SCIR）以及一个多任务双语（中英文）自我校正数据集MBSC，包含超过10万条记录。通过其双路径自我校正模块和反馈驱动优化，SCIR框架实现了与现有LLM和IE系统的即插即用兼容性，从而大幅降低了训练成本。同时，MBSC数据集通过间接提炼GPT-4的能力到信息抽取结果检测模型中，解决了偏好对齐的挑战。 实验结果显示，在命名实体识别、关系提取和事件抽取三项关键任务上，SCIR方法超越了现有的最先进的IE方法，平均提高了基于跨度的Micro-F1分数5.27%，并且相比基准方法将训练成本降低了87%。这些进展不仅提升了信息抽取系统的灵活性和准确性，还为轻量级且高效的IE范式铺平了道路。
URL
https://arxiv.org/abs/2512.12337
