Encoder models outperform LLMs for Indic language NER

A new study published on arXiv investigates the effectiveness of generative versus encoder-based models for Named Entity Recognition (NER) across eleven Indic languages. The research, conducted on the Naamapadam benchmark, found that encoder-based models like mBERT and XLM-R significantly outperformed generative architectures, including fine-tuned LLMs such as Gemma-2-2B. The study identified distinct language clusters based on model performance and offered deployment guidelines for low-resource NLP. AI

IMPACT Identifies limitations of current generative LLMs for low-resource languages and highlights the continued strength of encoder models for specific NLP tasks.

RANK_REASON Research paper published on arXiv detailing empirical study of NLP models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Gemma 2-2B
• Hugging Face
• Indic languages
• multilingual-BERT
• Named Entity Recognition
• XLM-RoBERTa

• paper
• other

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