跨语言关系抽取与大语言模型：罗马尼亚语的零样本、少样本和微调评估
Cross-lingual Relation Extraction with Large Language Models: Zero-Shot, Few-Shot, and Fine-Tuned Evaluation on Romanian
AI总结：
通过自动翻译数据集结合大语言模型推理，研究罗马尼亚语的跨语言关系抽取，评估Gemma 4 31B在零样本、少样本和QLoRA微调下的性能，并与四种编码器基线对比。
摘要：
低资源语言的关系抽取通常受限于缺乏标注语料库。我们通过将自动数据集翻译与大语言模型推理相结合，研究罗马尼亚语的跨语言关系抽取可行性。我们使用基于LLM的翻译流水线将SemEval-2010 Task 8基准从英语翻译成罗马尼亚语，并在零样本、少样本和QLoRA微调配置下评估Gemma 4 31B，与四种参数规模从125M到560M的编码器基线（XLM-RoBERTa base和large、罗马尼亚语BERT、RoBERT-large）进行对比。我们评估了两种任务形式：带标记实体的关系分类和端到端抽取。结果表明，在仅提示设置下，罗马尼亚语相比英语性能下降3到5个百分点；少样本提示相比零样本提升有限；QLoRA微调在两种语言上将宏F1分数提高了超过22个百分点，同时将跨语言差距从3.3个百分点缩小到1.4个百分点。编码器基线在罗马尼亚语上达到QLoRA Gemma的1-4个百分点以内，尽管模型大小仅为后者的1/50到1/250，其中125M参数的罗马尼亚语单语BERT与278M参数的多语言XLM-R性能相当。因此，在计算资源重要的部署场景中，使用31B模型进行罗马尼亚语单任务关系抽取的理由较弱。我们发布了翻译后的数据集、评估代码和训练好的模型。
英文摘要：
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 configurations, against four encoder baselines spanning 125M to 560M parameters: XLM- RoBERTa (base and large), Romanian BERT, and RoBERT- large. We assess two task formulations: relation classification with marked entities and end-to-end extraction. Our results show that Romanian incurs a 3 to 5 percentage point (pp) drop relative to English in prompt-only settings, that few-shot prompting provides marginal gains over zero-shot, and that QLoRA fine-tuning improves macro F1-Score by more than 22 percentage points in both languages while reducing the cross-lingual gap from 3.3 to 1.4pp. The encoder baselines come within 1-4pp of QLoRA Gemma on Romanian despite being 50-250 times smaller, with monolingual Romanian BERT at 125M parameters matching multilingual XLM-R at 278M. The case for using a 31B model for single-task RE on Romanian is therefore weak in deployment scenarios where compute matters. We release the translated dataset, evaluation code, and trained models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
