Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
Researchers demonstrate that large language models can effectively handle relation extraction for low-resource languages through cross-lingual transfer and synthetic data generation. By translating English benchmarks to Romanian and evaluating Gemma 4 against smaller encoder models, the work reveals a modest 3-5 percentage point performance gap, suggesting LLMs offer a practical path for NLP tasks in underserved languages without expensive annotation efforts. This challenges the assumption that low-resource language tasks require prohibitively large labeled datasets.
Modelwire context
ExplainerThe paper's actual contribution is methodological: it shows that translating English benchmarks to Romanian and using synthetic data generation can close most of the performance gap between general-purpose LLMs and task-specific encoders. The modest gap itself is less surprising than the path to achieving it without expensive Romanian annotation.
This work sits alongside the dysarthric ASR case study from earlier this week, which also tackled underserved populations through targeted adaptation rather than massive labeled datasets. Both papers challenge the assumption that low-resource scenarios require prohibitively expensive data collection. However, the Romanian relation extraction work relies on synthetic data and cross-lingual transfer (English as a proxy), whereas the ASR work required actual speaker-specific audio, suggesting the viability of each approach depends heavily on whether high-quality source material exists in a related domain.
If the same Gemma 4 setup achieves comparable performance on a held-out Romanian relation extraction benchmark that was not derived from English translation (a native Romanian corpus), that confirms cross-lingual transfer is robust. If performance drops significantly, the gains may reflect benchmark artifacts rather than genuine cross-lingual capability.
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MentionsGemma 4 · XLM-RoBERTa · Romanian BERT · RoBERT · SemEval-2010 Task 8 · QLoRA
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