🇨🇿 BenCzechMark - Can your LLM Understand Czech?

🇵🇭 FilBench - Can LLMs Understand and Generate Filipino?

We Got Claude to Fine-Tune an Open Source LLM

CyrillicQA: The Influence of Phonetically Encoded Secret Language on LLM Performance

arXiv:2608.21462v1 Announce Type: cross Abstract: Due to the selection of their training data, large language models (LLMs) perform best on standard-language inputs from languages using the Latin alp...

Llama 3.1 - 405B, 70B & 8B with multilinguality and long context

KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMs

arXiv:2510.18368v2 Announce Type: replace Abstract: We present $\textbf{Korean SimpleQA (KoSimpleQA)}$, a benchmark for evaluating factuality in large language models (LLMs) with a focus on Korean cu...

Introducing the Open Leaderboard for Hebrew LLMs!

Introducing the Open Ko-LLM Leaderboard: Leading the Korean LLM Evaluation Ecosystem

Learning to reason with LLMs

The Limits of BPE Tokenization in Polish: Segmentation-Flexional Forms, Grammatical Anchoring, and First-Person Stability in Inflectional Language Models

The article examines how Byte‑Pair Encoding (BPE) tokenization handles Polish, an inflectional language, and finds that BPE tends to stabilize frequent surface fragments of grammatical exponents rather than true grammatical categories. It introduces the concept of grammatical form anchoring, showing that certain Polish verb forms can signal the speaking subject without an explicit pronoun, and highlights that language models may lack a stable grammatical "I" and can shift gender or mirror user forms. The study proposes Roclawski’s segmentation‑flexional forms as a diagnostic framework and suggests that more stable Polish modeling would require sublexical stabilization, anchoring grammatical form in the inflectional system, and maintaining the grammatical "I" in dialogue.

Introducing the Open Leaderboard for Japanese LLMs!

ReMova: Fine-tuning LLMs for English to Belarusian translation

The paper introduces ReMova, a pipeline for cleaning Belarusian data and fine‑tuning large language models (LLMs) for English‑to‑Belarusian translation. It uses a correction tool to handle the two orthographies of Belarusian, remove noise, filter out interference from other languages, and correct common misspellings found online. Ablation experiments on unfiltered data show that filtering benefits all fine‑tuned models, with LLM‑based models gaining about twice as much as a dedicated encoder‑decoder MT system, highlighting data quality as a key bottleneck for Belarusian MT.

The Open Arabic LLM Leaderboard 2