Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages

Mitodru Niyogi; Eric Gaussier; Arnab Bhattacharya

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arXiv · 2401.18034

Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages

Mitodru Niyogi·Eric Gaussier·Arnab Bhattacharya

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Abstract

Multilingual large language models (LLMs) are expensive to pretrain and often suffer from imbalances across languages and datasets, English-centric bias, tokenizer oversegmentation for morphologically rich low-resource languages, and the curse of multilinguality. We introduce PARAMANU, the first family of Indian-only autoregressive language models trained from scratch on open-source language-specific data for the five most spoken Indian languages: Bengali, Hindi, Marathi, Tamil, and Telugu. All models are designed for affordability and are trained on a single GPU with a budget under $1,000, allowing under-resourced researchers to build competitive language models. To address low-resource challenges, we develop morphology-aligned, low-fertility tokenizers, propose an interpolation-based method for token position indices in RoPE based scaling to train longer sequences efficiently. We also create instruction-tuning datasets in Bangla that are translated to the other four languages. Despite their small size (108M-367M parameters), Paramanu achieves a strong performance-efficiency tradeoff and outperforms most larger multilingual models across all five languages. Our collection is available at https://huggingface.co/collections/mitodru/paramanu.

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Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya. 2024-01-31. Paramanu: Compact and Competitive Monolingual Language Models for Low-Resource Morphologically Rich Indian Languages. https://arxiv.org/abs/2401.18034

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