@inproceedings{ju-etal-2024-globe,
title = "To the Globe ({TTG}): Towards Language-Driven Guaranteed Travel Planning",
author = "Ju, Da and
Jiang, Song and
Cohen, Andrew and
Foss, Aaron and
Mitts, Sasha and
Zharmagambetov, Arman and
Amos, Brandon and
Li, Xian and
Kao, Justine T and
Fazel-Zarandi, Maryam and
Tian, Yuandong",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.25",
pages = "240--249",
abstract = "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 {\textasciitilde}5seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates userrequests, 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 {\textasciitilde}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.",
}
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<abstract>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 ~5seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates userrequests, 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.</abstract>
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%0 Conference Proceedings
%T To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning
%A Ju, Da
%A Jiang, Song
%A Cohen, Andrew
%A Foss, Aaron
%A Mitts, Sasha
%A Zharmagambetov, Arman
%A Amos, Brandon
%A Li, Xian
%A Kao, Justine T.
%A Fazel-Zarandi, Maryam
%A Tian, Yuandong
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ju-etal-2024-globe
%X 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 ~5seconds to reply to the user request with guaranteed itineraries. To train TTG, we develop a synthetic data pipeline that generates userrequests, 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.
%U https://aclanthology.org/2024.emnlp-demo.25
%P 240-249
Markdown (Informal)
[To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning](https://aclanthology.org/2024.emnlp-demo.25) (Ju et al., EMNLP 2024)
ACL
- Da Ju, Song Jiang, Andrew Cohen, Aaron Foss, Sasha Mitts, Arman Zharmagambetov, Brandon Amos, Xian Li, Justine T Kao, Maryam Fazel-Zarandi, and Yuandong Tian. 2024. To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 240–249, Miami, Florida, USA. Association for Computational Linguistics.