arXiv:2510. 07074v2 Announce Type: replace-cross Abstract: Instruction tuning has become a key technique for enhancing the performance of large language models, enabling them to better follow human prompts.
By Fred Philippy, Laura Bernardy, Siwen Guo, Jacques Klein, Tegawend\'e F. Bissyand\'e
The paper reports a submission to the WMT 2026 Multilingual Instruction Shared Task, where the authors fine‑tune Qwen3‑8B with LoRA on organiser‑provided data, EuroAlpaca, and CrossEuroAlpaca. Their system focuses on open‑ended question answering in ten target languages and shows a 7.8 % relative gain in Gemma‑4 judge score for target‑language QA‑OEG, as well as 41.5 % macro‑averaged ROUGE‑L and 2.9 % BERTScore‑F1 improvements on the Aya Evaluation Suite. However, the gains are task‑ and language‑specific, with some declines in context‑grounded QA and non‑target languages.
By Aleix Sant, Jordi Luque, Carlos Escolano
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:2607.00890v2 Announce Type: replace
Abstract: Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synth...
By Maximilian Idahl, J\"org Tiedemann, Sampo Pyysalo, David Salinas, Tomasz Galica, Shenbin Qian, Tudor Nicolae Mateiu, Zihao Li, Anna Lokrantz, Fedor Vitiugin, Andr\'e F. T. Martins, Jenna Kanerva, Filip Ginter, Matthias Lindemann, Tim Isbister, Birger Moell, Jonas Lindh, Jan Haji\v{c}, Jenia Jitsev, Andrey Kutuzov, Stephan Oepen, Gema Ram\'irez-S\'anchez
arXiv:2604.13286v2 Announce Type: replace
Abstract: Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing...
By Mehak Dhaliwal, Shashwat Chaurasia, Yao Qin, Dezhi Hong, Thomas Butler
Open web-scale pre-training corpora remain concentrated in English, limiting multilingual LLM development. We introduce MultiSynt/MT, an open synthetic parallel corpus with approximately 4.