3arXiv cs.CL (Computation and Language)·
Translation-based fine-tuning of English BERT compared to native-language models across six NLP tasks and five languages
A new arXiv preprint systematically compares translation-based fine-tuning of English BERT against native-language BERT models across six NLP tasks using datasets from Bulgarian, Chinese, Dutch, Italian, and Russian. The translation approach was comparable or superior in 53.3% of cases, with strongest gains in Question Answering, POS Tagging, and NLI, but weaker performance on Named Entity Recognition and Hate Speech Detection. Results suggest translation-based fine-tuning is most effective for syntactic tasks and typologically English-close languages like Dutch, offering a resource-efficient path for low-resource NLP.
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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.
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.
Forgotten Words: Benchmarking NeoBERT for Dementia Detection in Low-Resource Conversational Filipino and English Speech
This paper presents the first NLP-based dementia detection study for Filipino speech, constructing a parallel bilingual dataset of 4,000 DementiaBank-derived transcripts with manual Filipino translations. Five model families are evaluated across monolingual, zero-shot cross-lingual, and bilingual fine-tuning settings. English-trained BERT degrades sharply on Filipino (Macro-F1 = 0.455), but bilingual fine-tuning recovers performance to Macro-F1 = 0.969–0.973 across all transformer models. The key finding is that multilingual clinical NLP performance is driven by linguistic coverage during training rather than model scale or architecture.
Ancient Greek to Modern Greek Machine Translation: Novel Benchmark and Fine-Tuning Experiments
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.
Reinforcement learning with verifiable rewards improves small-model legal machine translation
Researchers evaluate multiple training paradigms for legal machine translation, comparing supervised fine-tuning and reinforcement learning with verifiable rewards (RLVR) on small models (Qwen3.5 4B/9B, Gemma 3 12B) against frontier reasoning models. Using the Swiss multilingual legal system as a testbed, they find RLVR outperforms SFT for legal NMT and brings small models close to frontier performance, though a gap remains. The study also observes diminishing returns from re-training as model size increases. Code and models are publicly released.
Sub-billion parameter SLMs outperform zero-shot GPT-5.4 and Claude Sonnet 4.6 on relation extraction benchmarks
A new arXiv paper demonstrates that small language models (360M–3B parameters) fine-tuned on task-specific data can substantially outperform zero-shot frontier LLMs on relation extraction tasks. The best sub-billion model, Qwen2.5-0.5B fine-tuned on pooled general-domain data, achieves micro-F1 of 0.83 versus 0.69 for GPT-5.4 and 0.66 for Claude Sonnet 4.6 in zero-shot settings. The authors attribute the gains to task adaptation rather than model architecture, with a discriminative RoBERTa baseline also exceeding frontier models, and show that 4-bit quantized models deployable on consumer GPUs can match or beat proprietary API-based systems for this narrow task. The work provides evidence that for well-defined NLP tasks with available training data, compact adapted models offer a practical, private, and hardware-efficient alternative to frontier APIs.
Cross-lingual in-context learning source language selection challenges fine-tuning assumptions
A new arXiv paper conducts a broad empirical study of cross-lingual transfer in few-shot in-context learning (ICL), spanning seven tasks, six models, and a typologically diverse set of languages. The study finds that conventional heuristics from supervised fine-tuning — such as relying on linguistic similarity or data availability — do not consistently transfer to the ICL regime. The authors also analyze language confusion as a key obstacle in generative cross-lingual ICL and propose alternative heuristics for source language selection.
Towards Reliable Multilingual LLMs-as-a-Judge: An Empirical Study
This paper systematically investigates strategies for extending LLM-based automatic evaluation (LLMs-as-a-Judge) to multilingual settings, covering high-, mid-, and low-resource languages (English, Spanish, Basque). The authors compare instruction translation, monolingual vs. multilingual supervision, and model size, finding that fine-tuned smaller models can match proprietary models when in-domain data is available, while zero-shot larger models are preferable out-of-domain. Two meta-evaluation datasets are extended to Spanish and Basque, and all data and code are publicly released.