Raising Bars, Not Parameters: LilMoo Compact Language Model for Hindi
This addresses linguistic inequalities in NLP for Hindi speakers, offering a transparent and reproducible alternative to opaque multilingual models, though it is incremental as it focuses on one language.
The paper tackles the underrepresentation of low-resource languages like Hindi in NLP by introducing LilMoo, a 0.6-billion-parameter Hindi language model trained from scratch, which outperforms comparably sized multilingual baselines such as Qwen2.5-0.5B and Qwen3-0.6B in evaluations.
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 curated English data. Using this dataset, we explore various training recipes for small-scale language models. Across comprehensive evaluation suites, LilMoo consistently outperforms comparably sized multilingual baselines such as Qwen2.5-0.5B and Qwen3-0.6B, demonstrating that well-designed language-specific pretraining can rival large multilingual models at the sub-billion-parameter range.