arXiv:2609.18284v1 Announce Type: new
Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian...
By M\'aty\'as Osv\'ath, Enik\H{o} H\'eja, No\'emi Ligeti-Nagy
arXiv:2606. 18717v1 Announce Type: cross Abstract: Turkish is agglutinative: meaning is carried by morphemes, yet the subword tokenizers that drive modern language models split words by corpus statistics, fragmenting semantically loaded suffixes and -- in the case of WordPiece and rule-based analyzers -- failing to decode their output back to the original text.
By Tolga \c{S}akar
arXiv:2610.09152v1 Announce Type: new
Abstract: Multilingual LLM benchmarks omit Slovak, a morphologically rich West Slavic language of five million speakers, or cover it only by machine translation....
By Marek \v{S}uppa, Ivan Vykopal, Andrej Ridzik, Kristi\'an Sopkovi\v{c}, Nat\'alia K\v{n}a\v{z}ekov\'a, Jaroslav Kop\v{c}an, Miroslav Bl\v{s}t\'ak, Vikt\'oria Ondrejov\'a, Daniel Hl\'adek, Michal Gregor, Martin Tamajka, Mari\'an \v{S}imko
The paper evaluates a 307‑million‑parameter multilingual cross‑encoder, Ru‑Jev, on Russian‑language tasks, comparing its original and an augmented release that presents routing labels in multiple surface forms. Experiments show that the augmentation improves accuracy on unfamiliar ordinary words, borrowed jargon, and office slang—up to 0.24 points—while providing no benefit for abbreviations or opaque codes. The study also highlights evaluation hazards and notes that increased accuracy often coincides with reduced risk‑coverage, especially with many candidate labels.
By Vladimir Beskorovainyi
Pearl-Chat is an 8.5‑million‑parameter causal decoder‑only transformer built from first principles in JAX and Flax NNX, specifically engineered for the low‑resource Bantu language Luganda. It uses a byte‑level BPE tokenizer trained from scratch on curated Luganda corpora, achieving a two‑fold compression improvement over larger models like LLaMA‑3 and Gemma. The model is trained in two stages—pre‑training on diverse Luganda text and instruction fine‑tuning on dialogues—reaching a validation perplexity of 1.20 and high ROUGE‑L and BLEU scores on a held‑out diagnostic benchmark, with all weights, tokenizers, and code released openly.
By Wesley Kambale, Roland Sankara
Saidzoda‑Tajik‑E5‑Large is a 560 M‑parameter Tajik text embedder fine‑tuned from multilingual‑e5‑large‑instruct. The authors generated 235,140 question‑passage pairs from Tajik laws, tax documents, Wikipedia and web text, trained for one epoch with hard negatives, and mixed in English and Russian pairs to preserve multilingual performance. On unseen Tajik law and tax passages the model retrieves the correct answer in the top ten for 93.6 % and 94.5 % of questions, respectively, outperforming the base model and matching larger models on similar tasks.
By Tohir Saidzoda, Nekruz Maxudov, Bedil Karimov, Abubakr Abdullozoda, Amin Jalilov, Jonibek Kishvarov, Rahmonali Sharipov, Mariyamkhon Shodyeva