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UTP-Bench: Uncertainty-aware Travel Planning Benchmark· UTP-Bench: 面向不确定性的旅行规划基准

Large Language Models (LLMs) have recently demonstrated strong capabilities in automated travel itinerary generation. However, real- world travel planning is inherently uncertain: transportation delays, crowd fluctuations, and unexpected stochastic delays frequently inval- idate otherwise feasible schedules. Existing benchmarks like TravelPlanner and TripCraft assume deterministic environments, evaluating only static constraint satisfaction and ignoring whether generated plans remain robust when such uncertainties arise. To address this limitation, we introduce UTP-Bench1 , a large-scale benchmark for uncertainty-aware travel planning. The dataset integrates real-world travel data spanning 504 cities of India, including attractions, restau- rants, accommodations, and multi-modal trans- por

AI 解读论文

提出面向不确定性的大规模旅行规划基准UTP-Bench

核心方法
构建了一个包含印度504个城市真实旅行数据的大型数据集,涵盖景点、餐馆、住宿和多模式交通等信息,用于评估考虑不确定性的自动行程规划
适合谁读
研究者 / 工程师
要解决的问题
现有的旅行规划基准忽略了现实世界中的不确定性因素,导致生成的行程不够鲁棒
关键实验
未提供
主要贡献
提供了一个新的、考虑不确定性的旅行规划评估基准,促进行业发展和研究进步
意义与局限
该基准有助于提高旅行规划系统的实际适用性和可靠性,但可能需要更多实验验证其有效性和通用性
领域:cs.AI作者:Etcharla Revanth Rao、Priyanshu Karmakar、Shubhojit Mallick
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