Papers with real-world travel planning

2 papers
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)

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Challenge: Existing travel planning systems assume users provide explicit queries, limiting their practical utility.
Approach: They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries.
Outcome: The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging.
Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)

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Challenge: Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries.
Approach: They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios.
Outcome: The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities.

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