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TravelBench : Exploring LLM Performance in Low-Resource Domains

Domaine:

natural language processing

Type de record:

paperdataset
Créateur:
BilJin
Hôte:avatar
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. 10 pages, 3 figures

Visit

arxiv.org

Tasks

text classification

Tags

Computation and LanguageArtificial Intelligence

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