Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
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Computer Science > Computation and Language
arXiv:2608.26124 (cs)
[Submitted on 22 Jun 2026]
Title:Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
Authors:Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, Xiaoyu Shen
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Abstract:Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-ended. Traditional rule engines are brittle and costly to maintain, whereas unconstrained LLM agents lack the reliability and auditability required for financial decisions. We present a production-grade LLM-powered pricing system with a strict decision boundary: LLMs perform structured extraction and bounded policy/path selection, while all numeric pricing, including total-price computation, is executed deterministically. Policies are compiled into interpretable condition trees, enabling open-ended support for new clauses and evolving rules without code changes, while exposing auditable artifacts for human-in-the-loop control. Periodic fine-tuning on logged traces further improves tree induction and path matching. Deployed at a municipal state-owned tourism enterprise across 7 scenic sites and 12 business categories with 1,500+ operators and 1,000+ active policies, the system processed 3,960 orders in six months, reduced the order management team from 15-20 to 3, and cut per-order handling time from 10 minutes to <2 minutes.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.26124 [cs.CL] |
| (or arXiv:2608.26124v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.26124
arXiv-issued DOI via DataCite
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View a PDF of the paper titled Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework, by Ziqiang Zhang and 8 other authors
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