CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data

Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera-G\'omez, Sara Hincapie-Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, others · arXiv preprint arXiv:2601.18026 · 2026

Abstract

Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, especially on the noisy and heterogeneous web data often used to train multilingual language models. In this paper, we introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages. Many of the included languages have been previously under-served, making CommonLID a key resource for developing more representative high-quality text corpora. We show CommonLID's value by using it, alongside five other common evaluation sets, to test eight popular LID models. We analyse our results to situate our contribution and to provide an overview of the state of the art. In particular, we highlight that existing evaluations overestimate LID accuracy for many languages in the web domain. We make CommonLID and the code used to create it available under an open, permissive license.

How to cite

@article{suarez2026commonlid,
title={CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data},
  author={Suarez, Pedro Ortiz and Burchell, Laurie and Arnett, Catherine and Mosquera-G{\'o}mez, Rafael and Hincapie-Monsalve, Sara and Vaughan, Thom and Stewart, Damian and Ostendorff, Malte and Abdulmumin, Idris and Marivate, Vukosi and others},
  journal={arXiv preprint arXiv:2601.18026},
  year={2026}
}

References

See the full reference list in the paper.