CONSISTRE：用于文档级关系抽取的统一一致性感知框架及大语言模型
CONSISTRE: A Unified Consistency-Aware Framework for Document-Level Relation Extraction with Large Language Models
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中文总结 AI 辅助
研究针对文档级关系抽取中，大语言模型预测易违反关系约束的问题，提出CONSISTRE统一框架。通过推理时的约束感知等及训练时的知识蒸馏强化学习两条路径解决，实验表明该框架有效提升性能，缩小开源与专有模型差距，增强可靠性。
中文摘要 AI 辅助
文档级关系抽取旨在跨扩展上下文提取多个实体之间的关系，并保持预测三元组之间的一致性。尽管大语言模型在信息提取中展现出卓越推理能力，但它们对每个候选三元组独立生成预测，可能违反传递性、对称性和功能唯一性等基本关系约束，导致矛盾且不可靠的输出。我们提出CONSISTRE，一个用于文档级关系抽取的统一一致性感知框架，通过两条互补路径解决此限制。第一条路径在推理时针对黑箱大语言模型，结合约束感知提示、基于约束的验证和迭代自我反思来优化预测，无需特定任务微调。第二条路径通过知识蒸馏和强化学习管道将一致性知识注入较小的开源模型：强大教师的推理痕迹通过监督微调提炼到学生模型中，随后使用联合优化提取性能和关系一致性的复合奖励进行GRPO对齐。两条路径在统一一致性公式下涵盖了API可访问和本地可部署场景。在DocRED上的实验表明，两条路径均优于基线，推理时路径使用现成黑箱大语言模型实现有竞争力的F1，训练时路径以一小部分推理成本大幅缩小了7 - 8B开源模型与最先进专有大语言模型之间的差距。消融研究证实，显式一致性建模减轻了关系矛盾，并提高了基于大语言模型的文档级关系抽取在两种部署范式下的可靠性。
英文摘要
Document-level relation extraction (DocRE) aims to extract relations among multiple entities across extended contexts while maintaining consistency across predicted triples. Although large language models (LLMs) show remarkable reasoning capabilities in information extraction, their predictions are typically generated independently for each candidate triple and may violate fundamental relational constraints such as transitivity, symmetry, and functional uniqueness, leading to contradictory and unreliable outputs. We propose CONSISTRE, a unified consistency-aware framework for DocRE that addresses this limitation through two complementary tracks. The first operates at inference time for black-box LLMs, combining constraint-aware prompting, constraint-based verification, and iterative self-reflection to refine predictions without task-specific fine-tuning. The second injects consistency knowledge into smaller open-source models via a knowledge distillation and reinforcement learning pipeline: reasoning traces from a powerful teacher are distilled into a student via supervised fine-tuning, followed by GRPO alignment using a composite reward that jointly optimizes extraction performance and relational consistency. Together, the two tracks cover both API-accessible and locally deployable scenarios under a unified consistency formulation. Experiments on DocRED show that both tracks outperform their baselines, with the inference-time track achieving competitive F1 using off-the-shelf black-box LLMs and the training-time track substantially narrowing the gap between 7--8B open-source models and state-of-the-art proprietary LLMs at a fraction of their inference cost. Ablation studies confirm that explicit consistency modeling mitigates relational contradictions and enhances the reliability of LLM-based DocRE across both deployment paradigms.
发表机构
- Université de Sherbrooke（舍布鲁克大学）
机构由 AI 辅助整理，请以论文原文为准。
