@inproceedings{liu-etal-2025-pricinglogic,
    title = "{P}ricing{L}ogic: Evaluating {LLM}s Reasoning on Complex Tourism Pricing Tasks",
    author = "Liu, Yunuo  and
      Zhu, Dawei  and
      Al-Khalili, Zena  and
      Cheng, Dai  and
      Chen, Yanjun  and
      Klakow, Dietrich  and
      Zhang, Wei  and
      Shen, Xiaoyu",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.393/",
    pages = "7736--7745",
    ISBN = "979-8-89176-332-6",
    abstract = "We present PricingLogic, the first benchmarkthat probes whether Large Language Mod-els (LLMs) can reliably automate tourism-booking prices when multiple, overlapping farerules apply. Travel agencies are eager to of-fload this error-prone task to AI systems; how-ever, deploying LLMs without verified reliabil-ity could result in significant financial lossesand erode customer trust. PricingLogic com-prises 300 natural-language questions based onbooking requests derived from 42 real-worldpricing policies, spanning two levels of diffi-culty: (i) basic customer-type pricing and (ii)bundled-tour calculations involving interactingdiscounts. Evaluations of a line of LLMs re-veal a steep performance drop on the harder tier,exposing systematic failures in rule interpreta-tion and arithmetic reasoning. These resultshighlight that, despite their general capabilities,today{'}s LLMs remain unreliable for revenue-critical applications without further safeguardsor domain adaptation. Our code and dataset areavaliable in https://github.com/EIT-NLP/PricingLogic."
}