Who Benchmarks the Benchmarks? A Case Study of LLM Evaluation in Icelandic

Abstract

This paper evaluates current Large Language Model (LLM) benchmarking for Icelandic, identifies problems, and calls for improved evaluation methods in low/medium-resource languages in particular. We show that benchmarks that include synthetic or machine-translated data that have not been verified in any way, commonly contain severely flawed test examples that are likely to skew the results and undermine the tests' validity. We warn against the use of such methods without verification in low/medium-resource settings as the translation quality can, at best, only be as good as MT quality for a given language at any given time. Indeed, the results of our quantitative error analysis on existing benchmarks for Icelandic show clear differences between human-authored/-translated benchmarks vs. synthetic or machine-translated benchmarks.

Figures (2)

• Figure 1: Mean proportion and 95% confidence interval for each of the labels across annotations and benchmarks, arranged by OK proportion in descending order
• Figure 2: Label proportions per rater across the evaluated benchmarks