CommonLID: New Benchmark Reveals Language Identification Models Overestimate Accuracy on Web Data
Researchers have introduced CommonLID, a human-annotated benchmark covering 109 languages designed to evaluate language identification (LID) models on real-world web data. Testing eight popular LID models against CommonLID and five other evaluation sets revealed that existing benchmarks systematically overestimate LID accuracy, particularly for underserved languages. The findings have significant implications for the quality of multilingual corpora used to train large language models.
A large international research team has published CommonLID, a community-driven, human-annotated benchmark for evaluating language identification (LID) systems in the web domain, covering 109 languages—many of which have been historically underserved by existing tools. The study tested eight widely used LID models across CommonLID and five other standard evaluation sets, finding that current benchmarks tend to overestimate model accuracy, especially for lower-resource languages encountered in noisy, heterogeneous web text. Language identification is a foundational step in building multilingual corpora, which are in turn used to train multilingual language models, meaning errors at this stage can propagate throughout downstream NLP systems. The authors argue that the gap between reported and real-world LID performance is particularly consequential for efforts to build more inclusive and representative AI systems. CommonLID and the associated code have been released under an open, permissive license to support further research. The paper spans 18 pages with 8 tables and 5 figures, and has undergone at least one revision since its initial submission in January 2026.
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- arXiv cs.CLCenter
CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data
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