Benchmarking Arabic--Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLMs under Rich Morphology and Low Lexical Overlap
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Computer Science > Computation and Language
Title:Benchmarking Arabic--Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLMs under Rich Morphology and Low Lexical Overlap
Abstract:Arabic-Russian machine translation (MT) remains under-explored due to the rich morphology of Arabic and low lexical overlap between the two languages. We benchmark seven fine-tuned neural machine translation (NMT) models against four few-shot large language models (LLMs) on a 20k/5k/5k split of a new 15.47M-pair corpus. Fine-tuned NLLB-1.3B achieves the highest BLEU (16.3) and COMET (0.738). Aya-Expanse 8B leads the few-shot LLMs (BLEU 1.7 on 500 sentences, chrF 25.7), but all LLM scores remain far below the fine-tuned NMT baselines. Error analysis identifies low lexical overlap as the dominant failure mode; among the worst translations, mT5-small produces 32% too-short outputs. Bootstrap tests confirm significant differences among most models. Our results demonstrate that fine-tuned NMT significantly outperforms few-shot LLMs for Arabic-Russian translation under low-resource conditions.
| Comments: | Preprint. 20 pages, 1 figure, 15 tables |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.29559 [cs.CL] |
| (or arXiv:2609.29559v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.29559
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Mullosharaf Arabov Am [view email][v1] Wed, 26 Aug 2026 11:22:24 UTC (228 KB)
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