--- title: "Bridging Scientific Heritage — Arabov 2026" description: "Arabic–Russian is a poorly resourced translation pair." authors: ["Mullosharaf K. Arabov"] year: 2026 venue: "arXiv:2606.30943" source: "https://arxiv.org/abs/2606.30943" doi: "10.48550/arxiv.2606.30943" arxiv: "2606.30943" licence: "arXiv — non-exclusive license to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/arabov-2026-bridging-scientific-heritage-arabic-russian/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/arabov-2026-bridging-scientific-heritage-arabic-russian/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/2/) / Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer # Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer **Authors:** [Mullosharaf K. Arabov](https://md.research.elamur.ai/free/en/authors/mullosharaf-k-arabov/) · **Year:** 2026 · **Venue:** arXiv:2606.30943 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/2606.30943) · [PDF](https://arxiv.org/pdf/2606.30943) · [DOI 10.48550/arxiv.2606.30943](https://doi.org/10.48550/arxiv.2606.30943) · [arXiv 2606.30943](https://arxiv.org/abs/2606.30943) · [OpenAlex](https://openalex.org/W7166872714) **Citations:** 0 citations (openalex-snapshot · 2026-10-14) · [2](#cited-by) in our library, of the 140884 papers whose bibliographies we read ## Summary Arabic–Russian is a poorly resourced translation pair. Its existing parallel data is mostly religious, conversational or general text, with almost no scientific terminology. Arabov fills this gap with a benchmark for translating scientific Arabic into Russian. He assembled a mixed parallel corpus of about 27,000 pairs (26,878). Scientific abstracts are combined with six general sources: religion, news, conversations, dictionaries, the Bible and Tatoeba. Three multilingual models of increasing size were adapted with LoRA/QLoRA at ranks 8 to 64: mT5-base, NLLB-200-distilled-1.3B and Qwen2.5-7B-Instruct. They were scored with BLEU, chrF, BERTScore and COMET. The best system was Qwen2.5-7B with QLoRA at rank 8, which clearly beat its own zero-shot output. Three-shot prompting brought no gain, and the sub-1B mT5 stayed weak even after tuning. The author concludes that both task-specific tuning and a minimum model size matter. Models, corpus and evaluation code are released. This gives a reproducible baseline for an underserved pair and for cross-community scientific exchange. ## Key points - The corpus has 26,878 training examples, built from scientific abstracts plus six general-domain sources. The test set has 1,500 sentences from scientific abstracts. - Best result: Qwen2.5-7B-Instruct with QLoRA rank 8 reached BLEU 23.15, chrF 43.89, BERTScore 0.906 and COMET 0.758. That is +4.36 BLEU and +0.051 COMET over zero-shot. - NLLB-200-distilled-1.3B with LoRA rank 32 reached 21.95 BLEU, a good trade-off between quality and cost. - mT5-base (580M) stayed unacceptable but improved steadily up to rank 64. Fine-tuning gains for the larger models ranged from +2.13 to +4.36 BLEU, while training under 1% of parameters. - Limitations: - The references are Gemma-3-4B translations, checked by experts on a random sample of 50 sentences. - All systems produced translations 2.1–3.1 tokens shorter than the references. ## Interesting ideas - Pick models from three distinct size regimes to locate the smallest capacity that reaches acceptable quality, instead of only testing one large model. — For Mova (learn): Run the same sweep over gparse sizes to find the cheapest parser that still meets a target LAS, and justify model size with data. - Sweep adapter rank together with model size. In this paper small models kept gaining from higher ranks, while ~1B models leveled off around rank 32. — For Mova (tree): When adapting gparse to new treebanks or UD dialects, tune adapter capacity relative to base-model size rather than using a fixed default. - Use another LLM's output as the reference set, audit a small random sample with experts, and trust mainly the relative comparisons. — For Mova (learn): Treat LLM-teacher silver data the same way: spot-check a fixed random sample per tier and report it as a validation signal, not ground truth. - Report a prompting-only baseline (zero-shot and few-shot) next to fine-tuning, so the benefit of training is measured rather than assumed. — For Mova (learn): Add "LLM prompted without training" as a standing control in evaluations, alongside the negative controls. - Track the length gap between system output and reference as a separate diagnostic, because it can quietly depress overlap metrics. — For Mova (back): Measure the length drift of the text → graph → text compressor and generation output to catch systematic omissions of content. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/) ## Cited by Cited by 2 papers of our library cite it (of the 140884 whose bibliographies we read); 1 of them have a page here, 1 not yet. - [Benchmarking Arabic--Russian Machine Translation: A Comparison of Fine-tuned NMT and Few-shot LLMs under Rich Morphology and Low Lexical Overlap](https://md.research.elamur.ai/free/en/papers/arabov-2026-benchmarking-arabic-russian-machine-translation/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @misc{arabov2026bridging, title = {{Bridging Scientific Heritage: An Arabic--Russian Parallel Corpus and LLM Benchmark for Sustainable Knowledge Transfer}}, author = {Arabov, Mullosharaf K.}, year = {2026}, doi = {10.48550/arxiv.2606.30943}, eprint = {2606.30943}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.2606.30943} } ``` - [← previous Bridging Linguistic Structure and Mechanistic Interpretability for Conceptual Interpretation in Language Models](https://md.research.elamur.ai/free/en/papers/aljaafari-2026-bridging-linguistic-structure-mechanistic-interpretability/) - [next → Bridging the Culture Gap: A Framework for LLM-Driven Socio-Cultural Localization of Math Word Problems in Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/azime-2026-bridging-culture-gap-framework-llm/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/2/) ## References 18 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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