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Generative vs. Encoder Models for Multilingual NER: A Comprehensive Empirical Study on Naamapadam

arXiv · AI, language, vision and robotics · article · Aug 30, 2026 · UTC

Language is humanity's most consequential technology, yet for over a billion speakers across India's twenty-two constitutionally recognised languages, its digital layer remains structurally incomplete. Named Entity Recognition (NER), the foundational step in transforming raw text into machine-interpretable knowledge, has been studied exhaustively for English but remains largely unsolved across most Indic languages. This paper presents a rigorous comparative study of generative and encoder-based neural architectures for NER on all eleven languages of the Naamapadam benchmark. We evaluate five c

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