用自然语言生成可保证最优的旅行计划，5秒出结果。
To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning
- 用微调大模型将自然语言转为符号形式，再由数学规划求解
- 翻译准确率达91%，行程成本接近最优解的97.9%
- 适合需要快速生成高质量旅行方案的用户
旅行规划是一项复杂耗时的任务，需满足航班、住宿、景点等多重互相关联的约束。本文提出 To the Globe (TTG) 实时演示系统，接收用户自然语言请求，通过微调的大语言模型将其转化为符号形式，并使用混合整数线性规划求解器生成最优行程。系统响应时间约5秒，能提供保证最优的行程方案。为训练TTG，我们构建了合成数据流水线，基于真实数据统计自动生成用户请求、航班与酒店信息的符号表示，无需人工标注；微调后的模型在回译评测中达到91%的精确匹配率，生成行程的成本与真实最优值之比为0.979。用户评估显示，行程满意度（NPS）稳定在35%-40%之间。
原文摘要 · Abstract (English)
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.
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