{"ID":2859512,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.06198","arxiv_id":"2510.06198","title":"Peeking inside the Black-Box: Reinforcement Learning for Explainable and Accurate Relation Extraction","abstract":"We introduce CogRE, a novel framework for relation extraction (RE), enhancing RE from both accuracy and explainability. The framework has two key components: (i) a reasoning mechanism that formulates relation extraction as a series of text-processing steps inspired by cognitive science, and (ii) an optimization process driven by a novel reinforcement learning (RL) reward function. Our framework introduces relation keywords and rewards generating such keywords using an automatically constructed keywords dictionary. This design addresses the lack of language-based explanations in traditional RE and provides supervision for explanation during RL training. Our experiments show that CogRE improves explanation quality by addressing two common failure patterns in one-shot RE: poor attention focus and limited one-shot learning capability. For example, our cognitive-structured reasoning with Qwen2.5-15B-Instruct on One-shot NYT29 achieves 24.65% F1, surpassing prior reasoning-based designs. Optimizing this approach with RL using our reward further improves performance by +23.46% (absolute). Further, models trained on NYT29 with our reward achieve a +16.9% F1 gain on out-of-distribution WIKIDATA. Finally, human evaluation shows that our best model generates relational keywords closely aligned with gold labels, increasing human explanation quality ratings by 54% (relative).","short_abstract":"We introduce CogRE, a novel framework for relation extraction (RE), enhancing RE from both accuracy and explainability. The framework has two key components: (i) a reasoning mechanism that formulates relation extraction as a series of text-processing steps inspired by cognitive science, and (ii) an optimization process...","url_abs":"https://arxiv.org/abs/2510.06198","url_pdf":"https://arxiv.org/pdf/2510.06198v2","authors":"[\"Xinyu Guo\",\"Zhengliang Shi\",\"Minglai Yang\",\"Mahdi Rahimi\",\"Mihai Surdeanu\"]","published":"2025-10-07T17:53:55Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.IR\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}