Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
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 Procedure (SOP, or rulebook) in natural language via a new Structured Prompt Optimization algorithm; (2) SO
The increasing complexity of large language models (LLMs) and the growing demand for transparency and interpretability in AI systems drives research into more explainable AI architectures.
This development offers a pathway to reconcile the performance of LLMs with the critical need for human-understandable reasoning and trust, particularly in sensitive applications.
The paradigm shifts from purely optimized black-box models or limited discrete prompts to models that derive performance from human-readable 'rulebooks' and offer local and global interpretability.
- · AI researchers and developers
- · Industries requiring explainable AI (e.g., finance, healthcare)
- · Users of complex AI systems
- · Developers of proprietary black-box AI
- · Systems lacking interpretability
- · Simple discrete prompt approaches
More transparent and trustworthy AI systems become available, integrating explainability directly into their core training.
Increased adoption of AI in sectors with high regulatory or ethical demands due to enhanced interpretability.
New regulatory frameworks may emerge, mandating interpretability features derived from such advances in AI design.
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Read at arXiv cs.LG