PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks

Yunuo Liu; Dawei Zhu; Zena Al-Khalili; Dai Cheng; Yanjun Chen; Dietrich Klakow; Wei Zhang; Xiaoyu Shen

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arXiv · 2510.12409

PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks

Yunuo Liu·Dawei Zhu·Zena Al-Khalili·Dai Cheng·Yanjun Chen·Dietrich Klakow·Wei Zhang·Xiaoyu Shen

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Abstract

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.

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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen. 2025-10-14. PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks. https://arxiv.org/abs/2510.12409

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