The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction

Open Relation Extraction (OpenRE) requires a model to extract unseen relations between head and tail entities from unstructured text for real-world applications. The core challenge of OpenRE lies in achieving reliable generalization to unseen relation types. Current OpenRE approaches either employ clustering techniques, which cannot generate relation labels and suffer from poor generalization, or rely on direct relation label generation via Large Language Models (LLMs), which lack sufficient discriminative capacity to distinguish easily confused relations. To address these limitations, we propose Reasoning-guided progressive OpenRE (ReaORE), a framework for performing relation extraction through coarse-to-fine relation reasoning. Specifically, ReaORE consists of two key stages: (i) relation filtering, which reasons over multiple aspects to understand relations and instances, yielding an initial relation set, and further supplements and filters relations via embedding-based similarity to ensure the target relation is included; (ii) relation prediction, which aims to predict the target relations from the above set via fine-grained comparative reasoning to better distinguish easily confused relations. Extensive experiments on two widely used OpenRE datasets demonstrate that ReaORE outperforms existing baselines.

Consistency-based repair is widely assumed to improve LLM-based document-level relation extraction (DocRE), but this assumption can fail when the benchmark itself is incompletely annotated. Whether such repair helps depends strongly on how closed the gold annotations are under the constraints it enforces. We introduce $Δ_{\rm closure}$, the rate at which gold annotations lack facts implied by the very rules used for repair, computable from the annotations and rules alone before any repair is run. The same typed constraints drive CONSISTRE, which applies them at inference time (constraint feedback for reflection, deterministic closure) and at training time (distillation and consistency-aware reinforcement learning). On 998 DocRED documents, whose Re-DocRED revision lowers $Δ_{\rm closure}$ from 1.71% to 0.257%, rescoring identical predictions reverses the method ranking: Self-Refine leads under DocRED, while a constraint-feedback variant moves from fifth place to first. On the near-closed DWIE, constraint feedback with chain-of-thought beats three generic reflection baselines by 0.046-0.107 micro-F1 ($p=0.0002$), and deterministic closure of the base output scores higher still at $3.6\times$ lower cost. A reinforcement-learning gain on DocRED (+0.025) does not survive Re-DocRED rescoring. Consistency repair pays off where annotations are closed, and $Δ_{\rm closure}$ tells in advance whether they are.

Implicit Discourse Relation Recognition (IDRR) remains a challenging task due to the requirement for deep semantic understanding in the absence of explicit discourse markers. A further limitation is that existing methods only predict relations without providing any supporting explanations. Recent advances in large language models (LLMs) have shown strong reasoning capabilities in both deep language understanding and natural language explanation generation. In this work, we propose a simple yet effective approach to distill the reasoning capabilities of LLMs into lightweight IDRR models to improve both performance and interpretability. Specifically, we first prompt an LLM to generate explanations for each training instance conditioned on its gold label. Then, we introduce a novel classification-generation framework that jointly performs relation prediction and explanation generation, and train it with the additional supervision of LLM-generated explanations. Our framework is plug-and-play, enabling easy integration with most existing IDRR models. Experimental results on PDTB demonstrate that our approach significantly improves IDRR performance, while human evaluation further confirms that the generated explanations enhance model interpretability. Furthermore, we validate the generality of our approach on sentiment classification and natural language inference