When Does Consistency Pay Off? Relational Constraints for LLM-based Document-Level Relation Extraction under Incomplete Annotations

arXiv:2607.24312 · cs.CL · Submitted 2026-07-27 · Read on arXiv

cs.CL

Submitted: 2026-07-27

Updated: 2026-10-13

Comments: 13 pages, 2 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Consistency — 114× in this paper · explained in 3 episodes
• GRPO — 46× in this paper · explained in 6 episodes
• Alignment — 40× in this paper · explained in 1 episode
• Trace — 25× in this paper · explained in 2 episodes
• Verification — 24× in this paper · explained in 2 episodes
• Distillation — 19× in this paper · explained in 2 episodes
• Fine-Tuning — 17× in this paper · explained in 5 episodes
• Knowledge Distillation — 12× in this paper · explained in 11 episodes
• Ablation — 11× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Reasoning Traces — 6× in this paper · explained in 3 episodes
• Reward Function — 6× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• F1 Score — 5× in this paper · explained in 3 episodes
• Aggregation — 4× in this paper · explained in 1 episode
• Group Relative Policy Optimization — 4× in this paper
• Iterative Refinement — 4× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 4× in this paper · explained in 6 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Entity Grounding — 3× in this paper · explained in 2 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Group Relative Policy Optimization (GRPO) — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Hallucination — 2× in this paper · explained in 3 episodes
• LoRA Fine-Tuning — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 2× in this paper · explained in 8 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• In-Context Learning — 1× in this paper · explained in 4 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Low-Rank Adaptation (LoRA) — 1× in this paper · explained in 9 episodes
• Multi-hop Reasoning — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Structured Reasoning — 1× in this paper · explained in 3 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Document-level Relation Extraction as Semantic Segmentation
• AutoRE: Document-Level Relation Extraction with Large Language Models
• Distilling the Knowledge in a Neural Network
• Constitutional AI: Harmlessness from AI Feedback
• Proximal Policy Optimization Algorithms
• Training language models to follow instructions with human feedback
• Direct Preference Optimization: Your Language Model is Secretly a Reward Model
• LoRA: Low-Rank Adaptation of Large Language Models
• Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

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