arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
By Samiratu Ntohsi, Neza David Tuyishimire, Anesu Kafesu, Marvin Ogore, Samuel Oluwajunwonlo Babalola, Oche Ankeli
arXiv:2607. 20241v1 Announce Type: cross Abstract: Culturally loaded translation poses unique challenges for machine translation (MT), as meanings are deeply embedded in socio-cultural contexts beyond surface linguistic forms.
By Yiming Wang, Jiayuan Di
The paper investigates how large language models (LLMs) can be used to generate synthetic data for low‑resource machine translation, focusing on Romansh, which has six distinct varieties. It finds that LLMs are better at translating from Romansh to a high‑resource language (German) than the reverse, creating an asymmetry that makes the direction of data augmentation critical. By generating synthetic translations into German rather than Romansh, the authors surpass a Gemini 3 Pro baseline on German‑Romansh translation, achieving a +23 BLEU improvement in the lowest‑resource variety and producing fluent translations in each Romansh variety according to human evaluation.
By Jannis Vamvas, Ignacio P\'erez Prat, Angela Heldstab, Dominic P. Fischer, Sina Ahmadi, Rico Sennrich
TransClean introduces a benchmark for identifying and removing translation noise—unwanted text such as language labels, explanations, or bilingual repetitions—from large language model (LLM) outputs. The authors analyzed 790,000 translations from 12 LLMs across 22 language pairs, cataloguing 12 common noise patterns and creating 9,900 noisy‑clean pairs (8,800 synthetic, 1,100 authentic). They evaluated two extraction methods—a span‑based approach using quality estimation models and an LLM‑prompted method—demonstrating the first systematic framework to assess and improve translation cleanliness.
By Shenbin Qian, Yves Scherrer
The paper investigates weight‑space merging of independently fine‑tuned multilingual machine translation models. Experiments show that merging is more successful when models share a target language, yet it still cannot match the peak performance of language‑specific checkpoints. When target languages differ, performance drops sharply, and analysis reveals that overlapping neuron activation and incompatible upper‑layer geometries cause these failures.
By Baban Gain, Trilok Nath Singh, Asif Ekbal
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.
By Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi