Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework
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...
| Authors | Ziqiang Zhang, Jing Ma, Zilong Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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