Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi
The dominance of large multilingual foundation models has widened linguistic inequalities in Natural Language Processing (NLP), often leaving low-resource languages underrepresented. This paper introduces LilMoo, a 0.6-billion-parameter Hindi language model trained entirely from scratch to address this gap. Unlike prior Hindi models that rely on continual pretraining from opaque multilingual foundations, LilMoo is developed through a fully transparent and reproducible pipeline optimized for limited compute environments. We construct a high-quality Hindi corpus (GigaLekh) filtered through both heuristic and learned (LLM-as-a-judge) methods, complemented by bilingual augmentation with curat...
| Authors | Shiza Fatimah, Aniket Sen, Sophia Falk et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
Connected in the knowledge graph
- Polygl0t/gigalekh-v1Dataset · cited by
- Polygl0t/tokenizersDataset · cited by
Models and datasets referencing this paper
- Polygl0t/gigalekh-v1Dataset
- Polygl0t/tokenizersDataset
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