arXiv Machine Learning
Aug 5

M-GATE: Multilingual Grammar, Accuracy in Translation, and Efficiency Benchmark for Large Language Models

arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.

By Tom\'a\v{s} Burkert, Angelika Peljak-{\L}api\'nska, David Zelen\'y
arXiv Computation and Language
Aug 25

LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data

LuxIT is a monolingual instruction‑tuning dataset for Luxembourgish, created by synthesizing instruction‑answer pairs from native texts using the DeepSeek‑R1‑0528 model and a quality‑assurance LLM‑as‑judge process. The resulting 227,507 high‑quality pairs were used to fine‑tune 14 LLMs (≤15 B parameters), yielding an average accuracy increase of +5.37 percentage points on standardized Luxembourgish proficiency exams and improvements in macro‑averaged F1 on nine of the fourteen downstream NLP tasks. These findings demonstrate that synthetic monolingual data can effectively enhance LLM performance in low‑resource languages and reveal the complex relationship between exam performance and practical NLP gains.

By Julian Valline, Cedric Lothritz, Siwen Guo, Jordi Cabot
arXiv Computation and Language
4d ago

TatBLiMP: A Benchmark of Linguistic Minimal Pairs for Tatar

TatBLiMP is the first benchmark of linguistic minimal pairs for the Tatar language, covering 16 morphosyntactic phenomena across 1,248 sentence pairs that differ by a single morpheme. Each pair contains one grammatical and one ungrammatical sentence, with the ungrammatical version generated by a deterministic perturbation and ratified by a native speaker. The benchmark evaluates models by comparing their assigned probabilities, allowing assessment without text generation or parsing, and tracks performance across from-scratch, cross‑lingual, and multilingual large language models.

By Ilshat Saetov, Dmitry Gaynullin