ChinaTravel: An Open-Ended Benchmark for Language Agents in Chinese Travel Planning

• URL: http://arxiv.org/abs/2412.13682v3
• Date: Fri, 30 May 2025 13:35:50 GMT
• Title: ChinaTravel: An Open-Ended Benchmark for Language Agents in Chinese Travel Planning
• Authors: Jie-Jing Shao, Bo-Wen Zhang, Xiao-Wen Yang, Baizhi Chen, Si-Yu Han, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-feng Li,
• Abstract summary: We introduce emphChinaTravel, the first open-ended benchmark grounded in authentic Chinese travel requirements.<n>We design a compositionally generalizable domain-specific language for scalable evaluation, covering feasibility, constraint satisfaction, and preference comparison.<n> Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a 37.0% constraint satisfaction rate on human queries.
• Score: 49.37899519520761
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the \emph{Language Agents} for real-world development. Among these, travel planning represents a prominent domain, combining complex multi-objective planning challenges with practical deployment demands. However, existing benchmarks often oversimplify real-world requirements by focusing on synthetic queries and limited constraints. We address the gap of evaluating language agents in multi-day, multi-POI travel planning scenarios with diverse and open human needs. Specifically, we introduce \emph{ChinaTravel}, the first open-ended benchmark grounded in authentic Chinese travel requirements collected from 1,154 human participants. We design a compositionally generalizable domain-specific language (DSL) for scalable evaluation, covering feasibility, constraint satisfaction, and preference comparison. Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a 37.0\% constraint satisfaction rate on human queries, a 10\times improvement over purely neural models. These findings highlight ChinaTravel as a pivotal milestone for advancing language agents in complex, real-world planning scenarios.

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