4arXiv cs.CL (Computation and Language)·
Cross-lingual transfer and LoRA adapter merging for biomedical NMT in low-resource Arabic-script languages
Researchers present a systematic study of healthcare-domain neural machine translation for four severely low-resource Arabic-script languages (Dari, Pashto, Sorani Kurdish, Urdu) using Arabic and Persian as higher-resource pivots. Three transfer strategies are evaluated: few-shot in-context learning, minimal supervised adaptation, and zero-data LoRA adapter merging — the last being novel in this setting. Supervised adaptation with just 500 sentences achieves near-pivot quality for Dari, while LoRA adapter merging reaches within 3.5 CHrF++ of supervised adaptation at zero additional data cost. Pashto and Sorani Kurdish remain below clinical deployment thresholds, exposing limits of cross-lingual transfer when structural distance is large.
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Synthetic data bootstrapping and LoRA fine-tuning for Q'eqchi' Mayan NMT without web scraping
Researchers introduce a data synthesis methodology for low-resource neural machine translation of Q'eqchi' Mayan, converting community-sourced dictionaries into a synthetic parallel corpus to avoid scraping target-language data. Using LoRA adapters on mT5-base, the approach achieves BLEU 42.02 on in-domain evaluation but only 0.59 against organic text, revealing a structural-semantic gap. An ablation with multi-task learning produced negative transfer, suggesting LoRA capacity limits conflict with auxiliary objectives. The study concludes synthetic bootstrapping is effective for structural priming but requires authentic data for semantic refinement via curriculum learning.
LoRA-based cascaded multimodal fusion framework for action recognition in medical training environments
A new arXiv preprint proposes a cascaded Low-Rank Adaptation (LoRA) framework for multimodal fusion applied to action and activity recognition in healthcare training settings. The architecture integrates modalities sequentially without retraining prior components, enabling scalable adaptation across datasets with different modality sets. Evaluation on two healthcare datasets (NurViD and Nurse Training) shows competitive performance against dataset-specific baselines. The work is a narrow application of parameter-efficient fine-tuning techniques to a specialized domain.
Cross-lingual transfer from Sinhala improves low-resource Dhivehi ASR
A new arXiv paper investigates cross-lingual transfer learning from Sinhala to improve automatic speech recognition for Dhivehi, a severely low-resource language of the Maldives. Across 17 experiments spanning five transfer paradigms, the best system—continual pre-training on Sinhala followed by Dhivehi fine-tuning with KenLM—achieves 12.89% WER, outperforming the Dhivehi-only baseline by 13.50% WER. A Turkish control experiment confirms that linguistic relatedness, not just additional data, drives the improvement.
DeltaMerge-LowRes: Composing Language and Task Weight Deltas for Low-Resource NLP Adaptation
A new arXiv preprint introduces DeltaMerge-LowRes, a method for adapting multilingual encoders to new languages and tasks in low-resource settings by training language and task deltas separately and composing them in weight space at inference. The paper proposes four composition rules including a novel cross-axis TIES variant that adapts the TIES-Merging algorithm to language/task axes rather than task/task axes. Evaluated across four task families and four African languages (158 cells), cross-axis TIES improves summarization by +4 to +7 chrF and QA F1 by +2.32, while sparsity-aware merging reduces calibration error by 36%. The work advances model merging techniques for low-resource multilingual NLP.
MaLoRA and MaRA: Selective state-space adapters improve multi-hop reasoning over LoRA
A new arXiv preprint proposes two adapter families — MaLoRA (token-level dynamic scaling via Mamba recurrence) and MaRA (context-level segment retrieval via cross-segment state tracking) — as improvements over standard LoRA for language model reasoning. Evaluated on three frozen backbones (Qwen-2.5-7B, Llama-3.1-8B, Gemma-2-9B) and two multi-hop QA benchmarks (MuSiQue, 2WikiMultihopQA), the methods yield average gains of +6.8 F1 (+10.5% relative) over LoRA, with up to +18.2% relative improvement on the hardest configuration. Token-level gains also transfer to RULER QA-2 under length-stress conditions.
Empirical study of LLM medical domain adaptation trade-offs in French QA
Researchers present a systematic comparison of continual pretraining (CPT), supervised fine-tuning (SFT), and their combination for adapting LLMs to French medical question answering. The study spans three model families, multiple sizes, and three initialization types, evaluating both multiple-choice and open-ended QA formats. Key findings: CPT+SFT yields the best MCQA scores but gains over SFT alone are often not statistically significant, making SFT a cost-effective default; for open-ended QA, CPT improves overlap metrics while SFT degrades generation quality. Cross-lingual transfer from French adaptation to English benchmarks is also demonstrated.
Benchmarking Local LLMs for Confidential Translation Workflows
This paper evaluates locally runnable LLMs (via Ollama) for offline, privacy-constrained translation workflows targeting freelance translators and smaller language service providers. The authors expand their Reeve Foundation corpus to include German and Simplified Chinese, then benchmark local models across four language directions against commercial NMTs (DeepL, Baidu), a frontier LLM (GPT-5.2), and professional local NMT systems. Results show substantial performance variation by language direction and model size, with the best local LLMs matching or exceeding local NMT systems and the frontier LLM, though falling short of top commercial NMTs. The study supports the viability of local LLMs for confidentiality-sensitive translation use cases.
Cross-lingual relation extraction for Romanian: QLoRA fine-tuning narrows gap but small encoders remain competitive
Researchers evaluate cross-lingual relation extraction for Romanian by translating the SemEval-2010 Task 8 benchmark and testing Gemma 4 31B under zero-shot, few-shot, and QLoRA fine-tuned settings against encoder baselines (XLM-RoBERTa, Romanian BERT, RoBERTa-large). QLoRA fine-tuning improves macro F1 by over 22 percentage points and reduces the English-Romanian cross-lingual gap from 3.3 to 1.4 pp, but encoder baselines of 125M–560M parameters come within 1–4 pp of the fine-tuned 31B model. The study concludes that large LLMs offer limited advantage over compact encoders for single-task relation extraction in compute-constrained deployment scenarios, and releases the translated dataset, code, and models.