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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.16456 |
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| _version_ | 1866909358814658560 |
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| author | JU, Da Jiang, Song Cohen, Andrew Foss, Aaron Mitts, Sasha Zharmagambetov, Arman Amos, Brandon Li, Xian Kao, Justine T Fazel-Zarandi, Maryam Tian, Yuandong |
| author_facet | JU, Da Jiang, Song Cohen, Andrew Foss, Aaron Mitts, Sasha Zharmagambetov, Arman Amos, Brandon Li, Xian Kao, Justine T Fazel-Zarandi, Maryam Tian, Yuandong |
| contents | Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attractions, and other travel arrangements. In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symbolic form via a fine-tuned Large Language Model, and produces optimal travel itineraries with Mixed Integer Linear Programming solvers. The overall system takes ~5 seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates user requests, flight and hotel information in symbolic form without human annotations, based on the statistics of real-world datasets, and fine-tune an LLM to translate NL user requests to their symbolic form, which is sent to the symbolic solver to compute optimal itineraries. Our NL-symbolic translation achieves ~91% exact match in a backtranslation metric (i.e., whether the estimated symbolic form of generated natural language matches the groundtruth), and its returned itineraries have a ratio of 0.979 compared to the optimal cost of the ground truth user request. When evaluated by users, TTG achieves consistently high Net Promoter Scores (NPS) of 35-40% on generated itinerary. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16456 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning JU, Da Jiang, Song Cohen, Andrew Foss, Aaron Mitts, Sasha Zharmagambetov, Arman Amos, Brandon Li, Xian Kao, Justine T Fazel-Zarandi, Maryam Tian, Yuandong Computation and Language Travel planning is a challenging and time-consuming task that aims to find an itinerary which satisfies multiple, interdependent constraints regarding flights, accommodations, attractions, and other travel arrangements. In this paper, we propose To the Globe (TTG), a real-time demo system that takes natural language requests from users, translates it to symbolic form via a fine-tuned Large Language Model, and produces optimal travel itineraries with Mixed Integer Linear Programming solvers. The overall system takes ~5 seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates user requests, flight and hotel information in symbolic form without human annotations, based on the statistics of real-world datasets, and fine-tune an LLM to translate NL user requests to their symbolic form, which is sent to the symbolic solver to compute optimal itineraries. Our NL-symbolic translation achieves ~91% exact match in a backtranslation metric (i.e., whether the estimated symbolic form of generated natural language matches the groundtruth), and its returned itineraries have a ratio of 0.979 compared to the optimal cost of the ground truth user request. When evaluated by users, TTG achieves consistently high Net Promoter Scores (NPS) of 35-40% on generated itinerary. |
| title | To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2410.16456 |