Computer Science > Computation and Language
arXiv:2506.15304 (cs)
[Submitted on 18 Jun 2025 (v1), last revised 9 Mar 2026 (this version, v2)]
Title:ConLID: Supervised Contrastive Learning for Low-Resource Language Identification
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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.
| Comments: | |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2506.15304 [cs.CL] |
| (or arXiv:2506.15304v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2506.15304
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Submission history
From: Negar Foroutan [view email][v1] Wed, 18 Jun 2025 09:35:33 UTC (9,317 KB)
[v2] Mon, 9 Mar 2026 20:16:21 UTC (9,311 KB)
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