Papers by Soumyabrata Chaudhuri

    2 papers
    TripTide: A Benchmark for Adaptive Travel Planning under Disruptions (2026.findings-acl)

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    Challenge: Recent work has shown the promise of Large Language Models (LLMs) for personalized, constraint-aware travel itinerary generation, but real-world travel often involves disruptions such as transit cancellations, weather-related closures, or overbooked attractions.
    Approach: They propose a benchmark to evaluate the ability of Large Language Models (LLMs) to revise travel itineraries under realistic disruptions.
    Outcome: The proposed benchmark evaluates the ability of Large Language Models (LLMs) to revise travel itineraries under real-world disruption scenarios.
    TripCraft: A Benchmark for Spatio-Temporally Fine Grained Travel Planning (2025.acl-long)

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    Challenge: Existing benchmarks such as TravelPlanner and TravelPlann+ rely on semi-synthetic data and lack key real-world constraints.
    Approach: They propose a spatio-temporally coherent travel planning dataset incorporating real-world constraints, including public transit schedules, public events, varied attraction categories, and user personas for enhanced personalization.
    Outcome: The proposed dataset significantly improves meal scheduling, improving performance from 61% to 80% in the 7-day scenario.

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