Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
📰 ArXiv cs.AI
Optimize LLMs for text classification using structured prompts and reinforcement learning to improve interpretability and performance
Action Steps
- Implement a structured prompt optimization framework using eXTC
- Apply reinforcement learning to fine-tune the model for better performance
- Evaluate the model's global and local interpretability over complex text
- Use the optimized model for text classification tasks
- Compare the results with traditional supervised fine-tuning methods
Who Needs to Know This
NLP engineers and researchers can benefit from this approach to improve the transparency and accuracy of their text classification models
Key Insight
💡 Structured prompt optimization and reinforcement learning can improve both global and local interpretability of LLMs for complex text classification
Share This
🚀 Boost text classification accuracy and interpretability with eXTC and reinforcement learning! 🤖
Key Takeaways
Optimize LLMs for text classification using structured prompts and reinforcement learning to improve interpretability and performance
Full Article
Title: Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
Abstract:
arXiv:2605.29076v1 Announce Type: cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability. We introduce eXTC (eXplainable Text Classifier) with three progressive stages: (1) learning a Standard Operating Procedu
Abstract:
arXiv:2605.29076v1 Announce Type: cross Abstract: LLMs have advanced text classification, yet existing paradigms face a trade-off: supervised (label only) fine-tuning is scalable but offers limited reasoning on complex text and lacks broader model transparency, while discrete prompt optimization offers human-readable instructions but struggles with performance and scalability. We introduce eXTC (eXplainable Text Classifier) with three progressive stages: (1) learning a Standard Operating Procedu
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