Kakugo: Distillation of Low-Resource Languages into Small Language Models

• URL: http://arxiv.org/abs/2601.14051v1
• Date: Tue, 20 Jan 2026 15:05:44 GMT
• Title: Kakugo: Distillation of Low-Resource Languages into Small Language Models
• Authors: Peter Devine, Mardhiyah Sanni, Farid Adilazuarda, Julieta Gil Loizaga, Barry Haddow,
• Abstract summary: Kakugo is a pipeline designed to train general-purpose Small Language Models (SLMs) for low-resource languages.<n>With a total generation and training cost of under $50 per language, Kakugo offers a method accessible for communities to develop language-specific AI.
• Score: 18.888596863202377
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Kakugo, a novel and cost-effective pipeline designed to train general-purpose Small Language Models (SLMs) for low-resource languages using only the language name as input. By using a large teacher model to generate synthetic prompts and translate instruction datasets, we produced training data and SLMs for 54 low-resource languages. Evaluations across a diverse set of general natural language processing tasks, including translation, classification, and question answering, demonstrate that our pipeline consistently improves performance over base models. With a total generation and training cost of under $50 per language, Kakugo offers an accessible method for communities to develop language-specific AI.

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