OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report
arXiv:2602.13139v3 Announce Type: replace Abstract: Language identification (LID) is an essential step in building high-quality multilingual datasets from web data. Existing LID tools (such as OpenLID or GlotLID) often struggle to identify closely related languages and to distinguish valid natural language from noise, which contaminates language-specific subsets, especially for low-resource languages. In this work we extend the OpenLID classifier by adding more training data, merging problematic language variant clusters, and introducing a special label for marking noise. We call this extended
The continuous improvement of AI models for language processing is a foundational ongoing effort, reflecting the rapid development and deployment of LLMs and global AI applications.
Improved language identification precision is critical for building high-quality, diverse multilingual AI datasets, which directly impacts the performance and inclusivity of AI systems.
The ability to accurately distinguish closely related languages and filter noise will lead to cleaner training data, resulting in more robust and less biased AI models, particularly for low-resource languages.
- · AI developers
- · Multilingual AI services
- · Users of low-resource languages
- · Data scientists
- · Providers of low-quality language data
- · AI systems with poor multilingual capabilities
More accurate language identification tools become available for widespread use in AI pipelines.
This leads to a significant increase in the quality and quantity of usable training data for a broader range of languages, especially those previously underrepresented.
Enhanced multilingual AI capabilities contribute to the development of more globally equitable and accessible AI applications, potentially reducing digital language barriers.
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