Tatar language benchmark tests model grammar skills
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Researchers created TatBLiMP, a new benchmark to evaluate how well language models understand the grammar of Tatar, a Turkic language spoken in Russia and other parts of Eurasia.
The benchmark uses pairs of sentences differing by only one grammatical element, testing if a model assigns a higher probability to the correct sentence. TatBLiMP includes 1248 sentence pairs covering 16 aspects of Tatar grammar and relies on attested sentences from Tatar literature paired with plausible but ungrammatical variations.
The benchmark reveals that model performance doesn't always improve with size. While smaller, from-scratch models trained specifically on Tatar achieved scores near 0.97, larger multilingual models with 30 to 120 billion parameters scored between 0.80 and 0.92. This suggests focused training on a language is more effective than simply increasing a model’s overall size. The creators acknowledge that TatBLiMP currently doesn’t fully capture the nuances of Tatar’s sound system, including vowel harmony and consonant assimilation.
They propose adding a second layer to the benchmark to address these features and provide a more comprehensive evaluation of language model capabilities.
