Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
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Computer Science > Computation and Language
Title:Structured Prompt Optimization Meets Reinforcement Learning for Global and Local Interpretability over Complex Text
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) SOP-grounded reasoning distillation from a large teacher LLM into a compact LM; and (3) expanding reasoning capabilities beyond the initial SOP via reinforcement learning. This design enables eXTC to provide (i) fast inference via a compact LM, with (ii) inference-time local reasoning traces, alongside a global, modular explanation of its learned domain rules, while (iii) significantly outperforming existing paradigms across diverse benchmarks in both classification performance and explanation quality, with stage-by-stage gains.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| ACM classes: | I.2.7; I.2.6 |
| Cite as: | arXiv:2605.29076 [cs.CL] |
| (or arXiv:2605.29076v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29076
arXiv-issued DOI via DataCite (pending registration)
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