ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

Negar Foroutan; Jakhongir Saydaliev; Ye Eun Kim; Antoine Bosselut

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arXiv · 2506.15304

ConLID: Supervised Contrastive Learning for Low-Resource Language Identification

Negar Foroutan·Jakhongir Saydaliev·Ye Eun Kim·Antoine Bosselut

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Abstract

Language identification (LID) is a critical step in curating multilingual LLM pretraining corpora from web crawls. While many studies on LID model training focus on collecting diverse training data to improve performance, low-resource languages -- often limited to single-domain data, such as the Bible -- continue to perform poorly. To resolve these imbalance and bias issues, we propose a novel supervised contrastive learning (SCL) approach to learn domain-invariant representations for low-resource languages. We show that our approach improves LID performance on out-of-domain data for low-resource languages by 3.2 percentage points, while maintaining its performance for the high-resource languages.

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Negar Foroutan, Jakhongir Saydaliev, Ye Eun Kim, Antoine Bosselut. 2025-06-18. ConLID: Supervised Contrastive Learning for Low-Resource Language Identification. https://arxiv.org/abs/2506.15304

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