TP-RAG：用于时空感知旅行规划的检索增强型大语言模型代理基准测试
TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planning
AI总结：
TP-RAG提出了一种用于时空感知旅行规划的检索增强型大语言模型基准测试，通过整合参考轨迹提升旅行计划的空间效率和兴趣点合理性，采用EvoRAG框架实现更高效的时空合规性。
摘要：
大语言模型（LLMs）在自动化旅行规划中展现出潜力，但往往在处理复杂的时空合理性方面存在不足。尽管现有基准主要关注基本计划的有效性，但忽略了诸如路线效率、兴趣点吸引力和实时适应性等关键因素。本文介绍了TP-RAG，首个专门针对检索增强型、时空感知旅行规划的基准测试。我们的数据集包含2348个真实世界旅行查询、85575个精细标注的兴趣点以及18784个高质量旅行轨迹参考，来源于在线旅游文档，支持动态和上下文感知的规划。通过广泛的实验，我们发现整合参考轨迹显著提高了旅行计划的空间效率和兴趣点合理性，但普遍性和鲁棒性仍面临挑战，由于存在冲突的参考和噪声数据。为解决这些问题，我们提出了EvoRAG，一种进化框架，能够有效结合多种检索轨迹与LLM的内在推理能力。EvoRAG实现了最先进的性能，相比从零开始和检索增强型基线，在时空合规性和减少常识性违规方面有所提升。我们的工作强调了将网络知识与LLM驱动的优化相结合的潜力，为更可靠和适应性强的旅行规划代理铺平了道路。
英文摘要：
Large language models (LLMs) have shown promise in automating travel planning, yet they often fall short in addressing nuanced spatiotemporal rationality. While existing benchmarks focus on basic plan validity, they neglect critical aspects such as route efficiency, POI appeal, and real-time adaptability. This paper introduces TP-RAG, the first benchmark tailored for retrieval-augmented, spatiotemporal-aware travel planning. Our dataset includes 2,348 real-world travel queries, 85,575 fine-grain annotated POIs, and 18,784 high-quality travel trajectory references sourced from online tourist documents, enabling dynamic and context-aware planning. Through extensive experiments, we reveal that integrating reference trajectories significantly improves spatial efficiency and POI rationality of the travel plan, while challenges persist in universality and robustness due to conflicting references and noisy data. To address these issues, we propose EvoRAG, an evolutionary framework that potently synergizes diverse retrieved trajectories with LLMs' intrinsic reasoning. EvoRAG achieves state-of-the-art performance, improving spatiotemporal compliance and reducing commonsense violation compared to ground-up and retrieval-augmented baselines. Our work underscores the potential of hybridizing Web knowledge with LLM-driven optimization, paving the way for more reliable and adaptive travel planning agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
