TravelBench : Exploring LLM Performance in Low-Resource Domains

• URL: http://arxiv.org/abs/2510.02719v1
• Date: Fri, 03 Oct 2025 04:44:34 GMT
• Title: TravelBench : Exploring LLM Performance in Low-Resource Domains
• Authors: Srinivas Billa, Xiaonan Jing,
• Abstract summary: We curated 14 travel-domain datasets spanning 7 common NLP tasks using anonymised data from real-world scenarios.<n>We report on the accuracy, scaling behaviour, and reasoning capabilities of LLMs in a variety of tasks.
• Score: 2.2917707112773593
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Results on existing LLM benchmarks capture little information over the model capabilities in low-resource tasks, making it difficult to develop effective solutions in these domains. To address these challenges, we curated 14 travel-domain datasets spanning 7 common NLP tasks using anonymised data from real-world scenarios, and analysed the performance across LLMs. We report on the accuracy, scaling behaviour, and reasoning capabilities of LLMs in a variety of tasks. Our results confirm that general benchmarking results are insufficient for understanding model performance in low-resource tasks. Despite the amount of training FLOPs, out-of-the-box LLMs hit performance bottlenecks in complex, domain-specific scenarios. Furthermore, reasoning provides a more significant boost for smaller LLMs by making the model a better judge on certain tasks.

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