Reinforcement learning to improve large language model-based automated code compliance systems
📰 ArXiv cs.AI
Improve automated code compliance systems with reinforcement learning and large language models to generate accurate computer-processable rules
Action Steps
- Fine-tune a large language model using supervised fine-tuning to instill domain knowledge
- Apply Group Relative Policy Optimization to improve the accuracy of generated intermediate representations
- Evaluate the performance of the framework using metrics such as accuracy and F1-score
- Compare the results with baseline models to demonstrate the effectiveness of the framework
- Refine the framework by adjusting hyperparameters and exploring different optimization techniques
Who Needs to Know This
AI engineers and researchers working on large language model-based automated code compliance systems can benefit from this framework to improve accuracy and reduce hallucinations
Key Insight
💡 Reinforcement learning can improve the accuracy of large language model-based automated code compliance systems by reducing hallucinations and generating accurate computer-processable rules
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Key Takeaways
Improve automated code compliance systems with reinforcement learning and large language models to generate accurate computer-processable rules
Full Article
Title: Reinforcement learning to improve large language model-based automated code compliance systems
Abstract:
arXiv:2606.22402v1 Announce Type: cross Abstract: Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucinated computer-processable rules. This paper introduces P4IR, a two-stage framework that uses supervised fine-tuning (SFT) to instill domain knowledge in an LLM, followed by Group Relative Policy Optimization (GRPO) to improve the accuracy of the generated intermediate representations in the form of
Abstract:
arXiv:2606.22402v1 Announce Type: cross Abstract: Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucinated computer-processable rules. This paper introduces P4IR, a two-stage framework that uses supervised fine-tuning (SFT) to instill domain knowledge in an LLM, followed by Group Relative Policy Optimization (GRPO) to improve the accuracy of the generated intermediate representations in the form of
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