| --- |
| license: cc-by-4.0 |
| configs: |
| - config_name: train |
| data_files: |
| - split: train |
| path: train.csv |
| - config_name: validation |
| data_files: |
| - split: validation |
| path: validation.csv |
| - config_name: test |
| data_files: |
| - split: test |
| path: test.csv |
| task_categories: |
| - text-generation |
| - text2text-generation |
| language: |
| - en |
| --- |
| |
| # TravelPlanner Dataset |
|
|
| TravelPlanner is a benchmark crafted for evaluating language agents in tool-use and complex planning within multiple constraints. (See our [paper](https://arxiv.org/pdf/2402.01622.pdf) for more details.) |
|
|
| ## Introduction |
|
|
| In TravelPlanner, for a given query, language agents are expected to formulate a comprehensive plan that includes transportation, daily meals, attractions, and accommodation for each day. |
|
|
| TravelPlanner comprises 1,225 queries in total. The number of days and hard constraints are designed to test agents' abilities across both the breadth and depth of complex planning. |
|
|
| ## Split |
|
|
| <b>Train Set</b>: 5 queries with corresponding human-annotated plans for group, resulting in a total of 45 query-plan pairs. This set provides the human annotated plans as demonstrations for in-context learning. |
|
|
| <b>Validation Set</b>: 20 queries from each group, amounting to 180 queries in total. There is no human annotated plan in this set. |
|
|
| <b>Test Set</b>: 1,000 randomly distributed queries. To avoid data contamination, we only provide the level, days, and natural language query fields. |
|
|
| ## Record Layout |
|
|
| - "org": The city from where the journey begins. |
| - "dest": The destination city. |
| - "days": The number of days planned for the trip. |
| - "visiting_city_number": The total number of cities included in the itinerary. |
| - "date": The specific date when the travel is scheduled. |
| - "people_number": The total number of people involved in the travel. |
| - "local_constraint": The local hard constraint, including house rule, cuisine, room type and transportation. |
| - "query": A natural language description or request related to the travel plan. |
| - "level": The difficulty level, which is determined by the number of hard constraints. |
| - "annotated_plan": A detailed travel plan annotated by a human, ensuring compliance with all common sense requirements and specific hard constraints. |
| - "reference_information": Reference information for "sole-planning" mode. |
|
|
| ## Citation |
|
|
| If our paper or related resources prove valuable to your research, we kindly ask for citation. Please feel free to contact us with any inquiries. |
|
|
| ```bib |
| @article{Xie2024TravelPlanner, |
| author = {Jian Xie, Kai Zhang, Jiangjie Chen, Tinghui Zhu, Renze Lou, Yuandong Tian, Yanghua Xiao, Yu Su}, |
| title = {TravelPlanner: A Benchmark for Real-World Planning with Language Agents}, |
| journal = {arXiv preprint arXiv: 2402.01622}, |
| year = {2024} |
| } |
| ``` |
