UTP-Bench：不确定性感知旅行规划基准
内容导读
大语言模型（LLM）近期已展现出自动生成旅行行程的强大能力。然而，现实旅行规划本质上存在不确定性：交通延误、客流波动及意外的随机延误，常使原本可行的日程失效。TravelPlanner和TripCraft等现有基准假设环境具有确定性，仅评估静态约束满足情况，而忽略生成的计划在……时是否仍具稳健性
对照原文
UTP-Bench: Uncertainty-aware Travel Planning Benchmark
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…
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相关来源
- 01arXiv · AI 文旅预印本原文
arxiv.org