We present PricingLogic, the first benchmark that probes whether Large Language Models(LLMs) can reliably automate tourism-related prices when multiple, overlapping fare rules apply. Travel agencies are eager to offload this error-prone task onto AI systems; however, deploying LLMs without verified reliability could result in significant financial losses and erode customer trust. PricingLogic comprises 300 natural-language questions based on booking requests derived from 42 real-world pricing policies, spanning two levels of difficulty: (i) basic customer-type pricing and (ii)bundled-tour calculations involving interacting discounts. Evaluations of a line of LLMs reveal a steep performance drop on the harder tier,exposing systematic failures in rule interpretation and arithmetic reasoning.These results highlight that, despite their general capabilities, today's LLMs remain unreliable in revenue-critical applications without further safeguards or domain adaptation. Our code and dataset are available at https://github.com/EIT-NLP/PricingLogic.
@article{arxiv.2510.12409,
title = {PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks},
author = {Yunuo Liu and Dawei Zhu and Zena Al-Khalili and Dai Cheng and Yanjun Chen and Dietrich Klakow and Wei Zhang and Xiaoyu Shen},
journal= {arXiv preprint arXiv:2510.12409},
year = {2025}
}