The Answer Lies Within: Self-Derived Rewards Enable Explainable Relation Extraction
cs.CL, cs.IR
Submitted: 2025-10-07
Updated: 2026-10-21
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Large Language Models for Mathematical Reasoning: Progresses and Challenges
- Towards Realistic Few-Shot Relation Extraction: A New Meta Dataset and Evaluation
- Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
- R1-RE: Cross-Domain Relation Extraction with RLVR
- Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum
- Rethinking Reasoning Quality in Large Language Models through Enhanced Chain-of-Thought via RL
- Think-J: Learning to Think for Generative LLM-as-a-Judge
- Instruct and Extract: Instruction Tuning for On-Demand Information Extraction
- Revisiting Large Language Models as Zero-shot Relation Extractors
- Deep Reinforcement Learning for Imbalanced Classification
- Improve Mathematical Reasoning in Language Models by Automated Process Supervision
- Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges
- Training language models to follow instructions with human feedback
- ADELIE: Aligning Large Language Models on Information Extraction
- Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge
- Relation Extraction with Explanation
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- Matching the Blanks: Distributional Similarity for Relation Learning
- Best of Both Worlds: A Pliable and Generalizable Neuro-Symbolic Approach for Relation Classification
- Revisiting Relation Extraction in the era of Large Language Models
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