TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a family of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can remove up to 96% of training tokens without harming quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this mixture outperform much larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.
By Milan Gritta, Patrik Lambert, Jihye Back, Amril Nazir
TranslatePsy-AfriSLM is an open‑source machine‑translation resource set for 19 Sub‑Saharan African languages, comprising curated parallel data, African‑specialized synthetic data, and a suite of fine‑tuned small language models (SLMs). The authors demonstrate that a unified quality‑estimation filtering can discard up to 96% of training tokens while preserving quality, and that filtered synthetic data dominates the quality‑efficiency Pareto frontier. Models trained on this curated mixture outperform larger systems such as TranslateGemma‑27B and Qwen3.5‑122B‑A10B, achieving superior performance with as few as 0.8 B parameters.
The study evaluates Arabic–Russian machine translation by comparing seven fine‑tuned neural machine translation (NMT) models with four few‑shot large language models (LLMs) on a new 15.47 million‑pair corpus split into 20k/5k/5k. Fine‑tuned NLLB‑1.3B achieves the best performance (BLEU 16.3, COMET 0.738), while the best few‑shot LLM, Aya‑Expanse 8B, scores only BLEU 1.7 on 500 sentences. Error analysis shows that low lexical overlap between Arabic and Russian is the main source of failures, and statistical tests confirm significant performance gaps between most models.
By Mullosharaf K. Arabov
Fine‑tuning large language models on parallel data can improve translation quality but also causes catastrophic forgetting of general capabilities. The study evaluates several forgetting‑mitigation methods—anchored to auxiliary data, model outputs, and base model parameters—using Llama 3.2 1B Instruct and Llama 3.1 8B Instruct on Arabic‑English and Spanish‑English translation tasks. Elastic Weight Consolidation best preserves general benchmark performance, yet only data mixing with control‑task examples maintains instruction‑following abilities such as formality and grammatical gender control, though these gains do not generalize to unseen prompts.
By Niklas Scholz, David Thulke, Abdallah Nasir, Will Allred, Evgeny Matusov, Hermann Ney
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
arXiv:2609.13615v1 Announce Type: new
Abstract: For our submission to the WMT26 Creole Language Translation Shared Task, we focus on machine translation (MT) models for Pacific creoles: Tok Pisin, Bi...
By Rapha\"el Merx, Nick Thieberger, Ekaterina Vylomova