mlsanityData to 14 Oct 2026

To the Globe (TTG): Towards Language-Driven Guaranteed Travel Planning

arXiv 2410.16456 ↗First posted 21 Oct 2024cs.CLcited by 21

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Abstract (arXiv)

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.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 21 of its 30 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

21 of 30 references.

References · 21

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,224
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  7. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  8. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
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  10. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  11. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  12. 2024TravelPlanner: A Benchmark for Real-World Planning with Language Agentscited by 392
9 more
  1. 2023Grammar-Constrained Decoding for Structured NLP Tasks without Finetuningcited by 197
  2. 2023Generalized Planning in PDDL Domains with Pretrained Large Language Modelscited by 139
  3. 2024NATURAL PLAN: Benchmarking LLMs on Natural Language Planningcited by 128
  4. 2024RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-Foldcited by 99
  5. 2024Premise Order Matters in Reasoning with Large Language Modelscited by 72
  6. 2024The SCIP Optimization Suite 9.0cited by 37
  7. 2024Easy Problems That LLMs Get Wrongcited by 30
  8. 2024TRIP-PAL: Travel Planning with Guarantees by Combining Large Language Models and Automated Plannerscited by 17
  9. 2024A Human-Like Reasoning Framework for Multi-Phases Planning Task with Large Language Modelscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 12; 2026: 8.

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  1. 2025PlanGenLLMs: A Modern Survey of LLM Planning Capabilitiescited by 83
  2. 2025Large Language Models for Planning: A Comprehensive and Systematic Surveycited by 40
  3. 2025A Survey of Optimization Modeling Meets LLMs: Progress and Future Directionscited by 31
  4. 2025HyperTree Planning: Enhancing LLM Reasoning via Hierarchical Thinkingcited by 14
  5. 2025TP-RAG: Benchmarking Retrieval-Augmented Large Language Model Agents for Spatiotemporal-Aware Travel Planningcited by 12
  6. 2025RETAIL: Towards Real-world Travel Planning for Large Language Modelscited by 9
  7. 2025OptiChat: Bridging Optimization Models and Practitioners with Large Language Modelscited by 8
  8. 2025SAC-Opt: Semantic Anchors for Iterative Correction in Optimization Modelingcited by 8
  9. 2024Towards Full Delegation: Designing Ideal Agentic Behaviors for Travel Planningcited by 7
  10. 2025LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferencescited by 5
  11. 2026Agents at Risk: How Users Unwittingly Undermine LLM Safetycited by 3
  12. 2026TREK: A Travel Reasoning and Evaluation Kit for LLM Agents in Complex Trip Planningcited by 2
9 more
  1. 2025Feedback-Induced Performance Decline in LLM-Based Decision-Makingcited by 1
  2. 2025TripScore: Aligning LLMs for Real-World Travel Planning via Expert-Calibrated Rewardcited by 1
  3. 2026Replan, Repair, or Edit? A Unified Empirical Evaluation of Travel Agents for Itinerary Revision under Resource Disruptionscited by 1
  4. 2025Persuasive or Neutral? A Field Experiment on Generative AI in Online Travel Planningcited by 0
  5. 2026ItinBench: Benchmarking Planning Across Multiple Cognitive Dimensions with Large Language Modelscited by 0
  6. 2026Revisiting the Travel Planning Capabilities of Large Language Modelscited by 0
  7. 2026Embark Now: User Demand Oriented Framework for Multi-day Urban Travel Itinerary Planningcited by 0
  8. 2026RELIC: Revealed Principles for Learning Interpretable Composable Skills in Multi-Agent Planningcited by 0
  9. 2026Behavior2Trip: Towards Personalized Travel Planning via User Behavior Trajectorycited by 0