Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
An interpretable LLM framework bridges the gap between unstructured travel orders and complex pricing policies by combining LLM-based structured extraction with deterministic numeric computation Policies are compiled into interpretable condition trees, enabling open-ended support for evolving rules without requiring code changes The system enforces a strict decision boundary: LLMs handle extraction and bounded policy selection, while all pricing calculations are executed deterministically Deploy
Analysis
TL;DR
- An interpretable LLM framework bridges the gap between unstructured travel orders and complex pricing policies by combining LLM-based structured extraction with deterministic numeric computation
- Policies are compiled into interpretable condition trees, enabling open-ended support for evolving rules without requiring code changes
- The system enforces a strict decision boundary: LLMs handle extraction and bounded policy selection, while all pricing calculations are executed deterministically
- Deployed at a municipal state-owned tourism enterprise, the system processed 3,960 orders across 7 scenic sites and 12 business categories over six months
- The deployment reduced the order management team from 15-20 to 3 and cut per-order handling time from 10 minutes to under 2 minutes
Why It Matters
This work demonstrates a practical production-grade approach to deploying LLMs in high-stakes financial decision-making contexts where reliability and auditability are non-negotiable. The strict separation between LLM-driven interpretation and deterministic computation offers a replicable blueprint for enterprises seeking to automate complex policy-driven workflows without sacrificing compliance or transparency.
Technical Details
- Hybrid Architecture: LLMs are constrained to structured information extraction and bounded policy/path selection, while all numeric pricing and total-price computation is performed deterministically, eliminating hallucination risks in financial calculations
- Interpretable Condition Trees: Pricing policies are compiled into condition trees that support open-ended clause addition and rule evolution without code modifications, while producing auditable artifacts for human-in-the-loop oversight
- Periodic Fine-Tuning: The system undergoes periodic fine-tuning on logged execution traces to continuously improve tree induction accuracy and path matching performance
- Scale of Deployment: Operates across 7 scenic sites, 12 business categories, with 1,500+ operators and 1,000+ active policies in a municipal state-owned tourism enterprise
- arXiv Classification: Computation and Language (cs.CL), submitted June 22, 2026
Industry Insight
- The strict decision-boundary design pattern—constraining LLMs to interpretation tasks while delegating computation to deterministic engines—should become a standard reference architecture for any enterprise deploying LLMs in regulated or financial domains
- Condition-tree compilation of natural-language policies offers a scalable alternative to traditional brittle rule engines, suggesting that policy-heavy industries (insurance, healthcare, travel) should prioritize this approach over building custom rule systems
- The dramatic operational efficiency gains (80%+ reduction in handling time, 80%+ headcount reduction) demonstrate that even modest LLM deployments in structured domains can yield transformative ROI, encouraging broader investment in production-grade LLM systems rather than experimental pilots
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