发表机构
Indian Institute of Technology Madras; L3Cube Labs（印度理工学院马德拉斯分校; L3Cube实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究比较基于BERT的模型与大语言模型在马拉地语低资源命名实体识别中的表现，通过在MahaNER数据集上微调MahaBERT-v2并与基线及通用模型对比，发现特定任务模型效果更佳，凸显专用架构对低资源语言处理的重要性。
AI 中文摘要
马拉地语等低资源语言的命名实体识别（NER）因标注资源有限和语言复杂性而颇具挑战。尽管近期大语言模型（LLMs）在自然语言处理任务中表现出色，但在低资源环境下对特定语言NER的有效性尚不确定。本研究在MahaNER数据集不同变体上微调MahaBERT-v2，并与现有MahaNER基线及通用LLMs比较。所有模型用标准指标在马拉地语NER测试数据集上评估。结果表明，微调后的基于MahaBERT的模型始终优于基线和所有评估的LLMs，F1分数在0.88至0.91之间，超过现有MahaNER模型（0.8843）且显著高于基于LLM方法（F1分数在0.57至0.69之间）。这些发现表明，在领域相关数据上训练的特定任务、语言聚焦模型在马拉地语NER中比通用LLMs更有效，凸显了低资源语言处理中专用架构的持续重要性。
英文摘要
Named Entity Recognition (NER) for low-resource languages such as Marathi remains a challenging task due to limited annotated resources and linguistic complexity. Although recent Large Language Models (LLMs) have demonstrated strong performance across a wide range of natural language processing tasks, their effectiveness for language-specific NER in low-resource settings remains uncertain. In this study, we fine-tune MahaBERT-v2 on different variants of the MahaNER dataset and systematically compare the performance of these models with an existing MahaNER baseline and prominent general-purpose LLMs, including Gemini, LLaMA-3.3-70B, and Gemma models. All models are evaluated on a Marathi NER test dataset using standard metrics of precision, recall, and F1-score. The experimental results show that the fine-tuned MahaBERT-based models consistently outperform both the baseline and all evaluated LLMs, with the fine-tuned models achieving F1-scores ranging from 0.88 to 0.91, surpassing the existing MahaNER model (0.8843) and significantly exceeding the performance of LLM-based approaches, whose F1-scores range from 0.57 to 0.69. These findings demonstrate that task-specific, language-focused models trained on domain-relevant data remain more effective than general-purpose LLMs for Marathi NER, highlighting the continued importance of specialized architectures for low-resource language processing.
