nllb-51ed11a7·2 events·first seen Aliases: NLLB
A preprint from arXiv evaluates multiple multilingual translation models—NLLB, mBART, and TamilLLaMA—on English-Tamil and Tamil-English translation across four datasets (NTREX, EnTamV2, WikiMatrix, PMIndia) using BLEU and chrF metrics. The study includes attention-based interpretability analysis and few-shot prompting experiments with TamilLLaMA. Key findings indicate dataset quality and domain alignment strongly affect performance, and that few-shot LLM approaches can produce structurally coherent Tamil translations despite limited supervised data.
Researchers introduce the AG-MG Parallel Corpus, a 132,481 sentence-pair dataset for Ancient Greek to Modern Greek machine translation, created via a pipeline combining web scraping, VecAlign with LaBSE embeddings, and Gemini 2.5 Flash-based alignment correction. The paper benchmarks NMT models (NLLB, M2M100) and a Greek LLM (Llama-Krikri-8B) under three fine-tuning strategies. Full-parameter fine-tuning of Llama-Krikri-8B achieves the best BLEU score of 13.16, while QLoRA-adapted M2M100-1.2B shows the largest relative gains (+10.3 BLEU). This represents the first comprehensive MT benchmark for this low-resource language pair.