ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
ReaORE tackles a fundamental weakness in open relation extraction by combining LLM reasoning with progressive refinement to handle unseen relation types. The framework moves beyond clustering's label-generation gap and pure LLM approaches that struggle with semantic confusion, using coarse-to-fine reasoning to improve generalization on real-world information extraction tasks. This addresses a practical bottleneck in knowledge graph construction and structured data mining where systems must handle novel relations without retraining.
Modelwire context
ExplainerReaORE's actual novelty sits in the coarse-to-fine reasoning pipeline itself, not just applying LLMs to open relation extraction. The framework explicitly stages reasoning to avoid semantic confusion between similar unseen relations, which pure end-to-end LLM calls tend to conflate.
This work sits alongside the semantic early-stopping paper from the same day, which also tackles inefficiency in iterative LLM loops. Both papers treat multi-step LLM workflows as optimization problems where intermediate structure matters more than raw inference. ReaORE's progressive refinement echoes the early-stopping logic: don't let the model wander; guide it through explicit stages. The reasoning efficiency gains here also connect to RolloutPipe's focus on training-time reasoning workloads, though ReaORE operates at inference time on a narrower task.
If ReaORE's gains hold on held-out relation types from real knowledge graphs (not synthetic benchmarks), and if downstream systems adopt the coarse-to-fine pattern for other extraction tasks (coreference, slot filling), then the framework has moved beyond a single-task contribution. If performance plateaus on relations semantically distant from training data, the approach has hit its actual ceiling.
Coverage we drew on
- Semantic Early-Stopping for Iterative LLM Agent Loops · arXiv cs.LG
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MentionsReaORE · Large Language Models · Open Relation Extraction
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