Cost-Efficient Estimation of General Abilities Across Benchmarks

Researchers have developed a new dataset called WILD to efficiently predict large language model performance on unseen tasks using fewer resources. By applying modified multidimensional item response theory and adaptive item selection techniques, they achieve accurate predictions with minimal data, reducing the evaluation cost by up to 85%. This approach offers developers a more efficient way to benchmark LLMs under budget constraints.

Read the full article at arXiv cs.CL (NLP)

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