Go to ACL ARR 2024 December homepageChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning
Abstract: Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of \emph{Language Agents}. Among these, travel planning represents a prominent domain, combining academic challenges with practical value due to its complexity and market demand. However, existing benchmarks fail to reflect the diverse, real-world requirements crucial for deployment. To address this gap, we introduce \emph{ChinaTravel}, a benchmark specifically designed for authentic Chinese travel planning scenarios. We collect the travel requirements from questionnaires and propose a compositionally generalizable domain-specific language that enables a scalable evaluation process, covering feasibility, constraint satisfaction, and preference comparison. Empirical studies reveal the potential of neuro-symbolic agents in travel planning, achieving a constraint satisfaction rate of 27.9\%, significantly surpassing purely neural models at 2.6\%. Moreover, we identify key challenges in real-world travel planning deployments, including open language reasoning and unseen concept composition. These findings highlight the significance of ChinaTravel as a pivotal milestone for advancing language agents in complex, real-world planning scenarios.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: benchmarking; evaluation; applications
Contribution Types: NLP engineering experiment, Data resources
Languages Studied: Chinese
Submission Number: 1968
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