Papers by Mitodru Niyogi

1 papers
Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages (2026.acl-long)

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Challenge: Multilingual large language models are expensive to pretrain and suffer from imbalances across languages and datasets.
Approach: They propose a family of Indian language-only autoregressive language models trained on open-source language-specific data for the five most spoken Indian languages.
Outcome: The proposed model outperforms most larger models up to 8B across all five languages.

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