@inproceedings{zhang-etal-2026-natural,
title = "Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework",
author = "Zhang, Ziqiang and
Ma, Jing and
Wang, Zilong and
Chen, Jiayuan and
Qiao, Yi and
He, Yu and
Zhang, Wei and
Cheng, Dai and
Shen, Xiaoyu",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.114/",
pages = "1668--1682",
ISBN = "979-8-89176-394-4",
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 {\ensuremath{<}}2 minutes."
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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 \ensuremath<2 minutes.</abstract>
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%0 Conference Proceedings
%T Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
%A Zhang, Ziqiang
%A Ma, Jing
%A Wang, Zilong
%A Chen, Jiayuan
%A Qiao, Yi
%A He, Yu
%A Zhang, Wei
%A Cheng, Dai
%A Shen, Xiaoyu
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F zhang-etal-2026-natural
%X 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 \ensuremath<2 minutes.
%U https://aclanthology.org/2026.acl-industry.114/
%P 1668-1682
Markdown (Informal)
[Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework](https://aclanthology.org/2026.acl-industry.114/) (Zhang et al., ACL 2026)
ACL
- Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng, and Xiaoyu Shen. 2026. Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 1668–1682, San Diego, California, USA. Association for Computational Linguistics.