CONSISTRE is a consistency‑aware framework for document‑level relation extraction that tackles contradictions in large language model predictions. It offers two tracks: an inference‑time track that refines black‑box LLM outputs through constraint‑aware prompting, verification, and self‑reflection, and a training‑time track that distills consistency knowledge into smaller open‑source models via supervised fine‑tuning and reinforcement learning. Experiments on DocRED show both tracks outperform baselines, with the inference‑time track matching competitive F1 scores and the training‑time track narrowing the performance gap to proprietary LLMs while reducing inference cost.
By Mingxuan Sun
arXiv:2601. 15037v2 Announce Type: replace-cross Abstract: Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas.
By Xiaonan Jing, Gongqing Wu, Xingrui Zhuo, Lang Sun, Jiapu Wang
arXiv:2606. 26986v1 Announce Type: cross Abstract: Open Relation Extraction (OpenRE) requires a model to extract unseen relations between head and tail entities from unstructured text for real-world applications.
By Xin Lin, Liang Zhang, Guoqi Ma, Hongyao Tu, Jinsong Su
arXiv:2608. 07838v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly supported response generation grounded in user-provided knowledge spanning heterogeneous structures.
By Shibo Chu, Yuze Liu, Tiehua Zhang, Zhishu Shen, Lianghua He, Haofen Wang, Zhijun Ding
arXiv:2609.24372v1 Announce Type: new
Abstract: In-context learning (ICL) based on large language models (LLMs) has shown promising potential in alleviating performance bottlenecks caused by the limi...
By Jingyu Wang, Shijie Wu, Fusheng Jin
The paper introduces an ontology-driven framework to measure and enforce structural consistency in document-level relation extraction (DocRE) datasets. It identifies that many distant supervision resources, such as DocRED, contain structural noise from violations of ontology constraints and logical contradictions, which negatively affect model predictions. By incorporating structural regularization during training, the authors demonstrate a reduction in logical contradictions and improved generalization performance.
By Laura Menotti, Stefano Marchesin, Gianmaria Silvello