Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
Learn how to apply large language models to cross-lingual relation extraction for low-resource languages like Romanian, and evaluate their performance in zero-shot, few-shot, and fine-tuned settings.
- Translate an English benchmark dataset to a low-resource language like Romanian using an LLM-based translation pipeline.
- Evaluate a large language model like Gemma 4 31B on the translated dataset in zero-shot, few-shot, and fine-tuned settings.
- Apply QLoRA fine-tuning to improve the model's performance on the target language.
- Compare the results of different evaluation settings to determine the most effective approach for cross-lingual relation extraction.
- Use the insights gained from this study to develop more accurate relation extraction models for low-resource languages.
NLP engineers and researchers working on low-resource languages can benefit from this study to improve relation extraction performance. The findings can be applied to develop more accurate models for languages with limited annotated corpora.
💡 Large language models can be effectively used for cross-lingual relation extraction in low-resource languages, with fine-tuning and few-shot learning leading to significant performance improvements.
✅ Improve relation extraction for low-resource languages like Romanian with large language models! ✅
Key Takeaways
Learn how to apply large language models to cross-lingual relation extraction for low-resource languages like Romanian, and evaluate their performance in zero-shot, few-shot, and fine-tuned settings.
Full Article
Abstract:
arXiv:2606.31718v1 Announce Type: cross Abstract: Relation extraction (RE) for low-resource languages is typically constrained by the lack of annotated corpora. We investigate the feasibility of cross-lingual RE for Romanian by combining automatic dataset translation with large language model (LLM) inference. We translate the SemEval-2010 Task 8 benchmark from English to Romanian using an LLM-based translation pipeline and evaluate Gemma 4 31B under zero-shot, few-shot, and QLoRA fine-tuned conf
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