Papers › ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning

ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning

18 Dec 2024arXiv:2412.13682archive 2025-07-28

Jie-Jing Shao, Xiao-Wen Yang, Bo-Wen Zhang, Baizhi Chen, Wen-Da Wei, Guohao Cai, Zhenhua Dong, Lan-Zhe Guo, Yu-Feng Li

Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of 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 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.

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calc_time_delta LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py official repository unverified MIT (permissive) · 2ab68efe91d4bf24 · report
city_lookup LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py official repository unverified MIT (permissive) · 4a50071d3c3731aa · report
city_names LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py official repository unverified MIT (permissive) · 83dd28641f911ace · report
convenient_restaurant LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py official repository unverified MIT (permissive) · 23eac122637ede76 · report
convenient_transport LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/preference.py official repository unverified MIT (permissive) · 0abf81e73b933293 · report
decode_numpy_dict LAMDASZ-ML/ChinaTravel/chinatravel/agent/utils.py official repository unverified MIT (permissive) · 9d3045c07c4343e4 · report
get_funcname_by_preference LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py official repository unverified MIT (permissive) · 1eb3756d467b8dbc · report
get_rank_with_value LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py official repository unverified MIT (permissive) · 9da02eac7da26244 · report
is_jsonable LAMDASZ-ML/ChinaTravel/chinatravel/agent/base.py official repository unverified MIT (permissive) · e41eb14033474ba5 · report
load_json_file LAMDASZ-ML/ChinaTravel/chinatravel/agent/utils.py official repository unverified MIT (permissive) · 71db62c521e435b8 · report
load_query LAMDASZ-ML/ChinaTravel/chinatravel/evaluation/rank.py official repository unverified MIT (permissive) · 8331cd223183af27 · report
normalize_lang LAMDASZ-ML/ChinaTravel/chinatravel/environment/language.py official repository unverified MIT (permissive) · 5a627af2661e5732 · report
normalize_run_name LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py official repository unverified MIT (permissive) · b79e07c34e67d9bf · report
resolve_agent_llm_name LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py official repository unverified MIT (permissive) · f52571c17760df0c · report
resolve_llm_name LAMDASZ-ML/ChinaTravel/chinatravel/agent/load_model.py official repository unverified MIT (permissive) · bb6cbda96b51d163 · report

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