Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer
This paper presents a benchmark for Arabic--Russian scientific translation, including a hybrid parallel corpus of about 27,000 sentence pairs compiled from scientific abstracts and general-domain texts. Three multilingual language models were fine-tuned using LoRA with various ranks. The Qwen2.5-7B model with QLoRA (rank 8) achieved the best results: BLEU 23.15, chrF 43.89, BERTScore 0.906, COMET 0.758, outperforming zero-shot baseline by +4.36 BLEU and +0.051 COMET. Few-shot prompting did not improve performance, indicating the necessity of domain-specific fine-tuning. The models, corpus, and evaluation code are released publicly, aiming to lower language barriers for scientific knowledge exchange between Arabic and Russian speakers and contributing to UN SDGs 9 and 17.
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[Submitted on 29 Jun 2026]
Title:Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer
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Abstract:Russian and Arabic are among the major languages of scientific communication. Language barriers impede the exchange of research results between these communities, which affects international collaboration and the progress of sustainability-related research. We present a benchmark for Arabic--Russian scientific translation. The benchmark includes a hybrid parallel corpus of about 27,000 sentence pairs, compiled from scientific abstracts and general-domain texts (religion, news, conversations). We fine-tune three multilingual language models -- mT5-base (580M parameters), NLLB-200-distilled-1.3B (1.3B), and Qwen2.5-7B-Instruct (7B) -- using LoRA with ranks 8, 16, 32, and 64. The Qwen2.5-7B model with QLoRA (rank 8) yields BLEU 23.15, chrF 43.89, BERTScore 0.906, and COMET 0.758. These are +4.36 BLEU and +0.051 COMET above the zero-shot baseline. Few-shot prompting with three examples does not improve performance, indicating that domain-specific fine-tuning is required. We release the models, the corpus, and the evaluation code. By lowering the language barrier for scientific texts, the work enables knowledge exchange between Arabic-speaking and Russian-speaking researchers. It contributes to sustainable partnerships (UN SDG 17) and innovation infrastructure (SDG 9), aligning with the conference's focus on technology-driven sustainable development.
Comments: Preprint
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.30943 [cs.CL]
(or arXiv:2606.30943v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.30943
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mullosharaf Arabov Am [view email] [v1] Mon, 29 Jun 2026 21:53:34 UTC (328 KB)
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