TravelEval: A Comprehensive Benchmarking Framework for Evaluating LLM-Powered Travel Planning Agents
Weiyi Chen, Shuaixiong Wang, Ziyun Gao, Kaichun Hu, Wangze Ni, Shimin Di, Chen Jason Zhang, Lei Chen
Why It Matters
What makes this one worth your time
This framework could significantly enhance the evaluation of travel planning agents, leading to improved models and applications in real-world scenarios.
TravelEval offers a novel approach to benchmark LLMs in travel planning through a comprehensive evaluation framework.
Summary
The paper introduces TravelEval, a benchmarking framework designed to evaluate LLM-powered travel planning agents by addressing limitations in existing benchmarks and providing a comprehensive six-dimensional evaluation framework.
Key contributions
- Introduction of a six-dimensional evaluation framework for travel planning.
- Creation of a realistic data sandbox for evaluating travel plans.
- Development of a simulation-based global evaluation method for comprehensive assessment.
Notable insights
- The six-dimensional evaluation framework allows for a more nuanced assessment of travel plans beyond simple compliance metrics.
- The use of a realistic data sandbox with authentic pricing and transportation data enhances the validity of the evaluations.
Possible limitations
- Potential challenges in the scalability of the framework to diverse travel scenarios or regions.
- Not stated in the abstract.
Abstract
arXiv:2606.01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existing benchmarks' limitations: 1) overemphasis on constraint compliance, neglecting multi-dimensional qualities like spatio-temporal cost; 2) datasets lacking real-world authenticity and coverage in key areas (e.g., lodging, transport); and 3) isolated daily plan assessments that miss critical details (e.g., the impact of daily accommodation and visit pacing) needed for entire plan's evaluation. To address this gap, we introduce TravelEval, a realistic and comprehensive benchmark. TravelEval features 1) a novel six-dimensional evaluation framework to holistically assess plans across accuracy, compliance, temporality, spatiality, economy, and utility dimensions; 2) a highly realistic data sandbox with precise accommodation pricing and authentic intercity transportation data; and 3) a simulation-based global evaluation method that emulates complete travel plans with API-integrated geographic information and fine-grained queuing time. Evaluating 12 mainstream approaches with TravelEval reveals several valuable insights, such that LLMs struggle with globally-optimized multi-dimensional planning (especially in spatio-temporal reasoning and budget compliance), and agentic reasoning strategies offer no consistent improvement. Concisely, TravelEval facilitates travel plan evaluation via grounded spatio-temporal emulation and comprehensive metrics, providing a robust foundation for advancing LLM-powered travel planning research and applications.