Who Benchmarks the Benchmarks? A Case Study of LLM Evaluation in Icelandic
arXiv:2603.16406v1 Announce Type: new 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.
arXiv:2603.16406v1 Announce Type: new 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.
Executive Summary
The article critiques current benchmarking methods for Large Language Models (LLMs) in Icelandic, highlighting issues with synthetic and machine-translated data. It argues that such methods can lead to flawed test examples, skewing results and undermining test validity. The study's quantitative error analysis reveals significant differences between human-authored and synthetic benchmarks, emphasizing the need for improved evaluation methods in low-resource languages.
Key Points
- ▸ Current LLM benchmarking methods for Icelandic are flawed
- ▸ Synthetic and machine-translated data can contain severe errors
- ▸ Human-authored benchmarks are more reliable than synthetic ones
Merits
Rigorous Error Analysis
The study's quantitative error analysis provides a thorough understanding of the differences between human-authored and synthetic benchmarks.
Demerits
Limited Scope
The study's focus on Icelandic may limit its generalizability to other low-resource languages.
Expert Commentary
The article's critique of current LLM benchmarking methods is timely and well-founded. The use of synthetic and machine-translated data can lead to a false sense of security regarding LLM performance, particularly in low-resource languages. The study's findings underscore the need for more rigorous and reliable evaluation methods, which can only be achieved through investments in high-quality, human-authored benchmarks. As the development of LLMs continues to accelerate, it is essential to prioritize the creation of accurate and reliable benchmarks to ensure that these models are truly effective and unbiased.
Recommendations
- ✓ Developers should prioritize the creation of human-authored benchmarks for low-resource languages
- ✓ Policymakers should invest in initiatives that promote the development of high-quality, human-authored benchmarks