Mangosteen: An Open Thai Corpus for Language Model Pretraining

Wannaphong Phatthiyaphaibun; Can Udomcharoenchaikit; Pakpoom Singkorapoom; Kunat Pipatanakul; Ekapol Chuangsuwanich; Peerat Limkonchotiwat; Sarana Nutanong

arXiv · 2507.14664

Mangosteen: An Open Thai Corpus for Language Model Pretraining

Wannaphong Phatthiyaphaibun·Can Udomcharoenchaikit·Pakpoom Singkorapoom·Kunat Pipatanakul·Ekapol Chuangsuwanich·Peerat Limkonchotiwat·Sarana Nutanong

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Abstract

Pre-training data shapes a language model's quality, but raw web text is noisy and demands careful cleaning. Existing large-scale corpora rely on English-centric or language-agnostic pipelines whose heuristics do not capture Thai script or cultural nuances, leaving risky material such as gambling content untreated. Prior Thai-specific efforts customize pipelines or build new ones, yet seldom release their data or document design choices, hindering reproducibility and raising the question of how to construct a transparent, high-quality Thai corpus. We introduce Mangosteen: a 47 billion-token Thai corpus built through a Thai-adapted Dolma pipeline that includes custom rule-based language ID, revised C4/Gopher quality filters, and Thai-trained content filters, plus curated non-web sources such as Wikipedia, Royal Gazette texts, OCR-extracted books, and CC-licensed YouTube subtitles. Systematic ablations using GPT-2 show the pipeline trims CommonCrawl from 202M to 25M documents while raising SEA-HELM NLG from 3 to 11; an 8B-parameter SEA-LION model continually pre-trained on Mangosteen then surpasses SEA-LION-v3 and Llama-3.1 by about four points on Thai benchmarks. We release the full pipeline code, cleaning manifests, corpus snapshot, and all checkpoints, providing a fully reproducible foundation for future Thai and regional LLM research.

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Wannaphong Phatthiyaphaibun, Can Udomcharoenchaikit, Pakpoom Singkorapoom, Kunat Pipatanakul, Ekapol Chuangsuwanich, Peerat Limkonchotiwat, Sarana Nutanong. 2025-07-19. Mangosteen: An Open Thai Corpus for Language Model Pretraining. https://arxiv.org/abs/2507.14664

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