# MixLID: Token-Level Language Identification in Mixed-Language Text

arXiv: https://arxiv.org/abs/2610.16601 · HTML: https://mlsanity.com/paper/2610.16601/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 36 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 21 of 21)

- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://mlsanity.com/paper/2309.09400/) (2023)
- [FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language](https://mlsanity.com/paper/2506.20920/) (2025)
- [An Open Dataset and Model for Language Identification](https://mlsanity.com/paper/2305.13820/) (2023)
- [Automatic Language Identification in Texts: A Survey](https://mlsanity.com/paper/1804.08186/) (2018)
- [AfroLID: A Neural Language Identification Tool for African Languages](https://mlsanity.com/paper/2210.11744/) (2022)
- [Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning](https://mlsanity.com/paper/2510.07300/) (2025)
- [An Expanded Massive Multilingual Dataset for High-Performance Language Technologies (HPLT)](https://mlsanity.com/paper/2503.10267/) (2025)
- [SMOL: Professionally translated parallel data for 115 under-represented languages](https://mlsanity.com/paper/2502.12301/) (2025)
- [MaskLID: Code-Switching Language Identification through Iterative Masking](https://mlsanity.com/paper/2406.06263/) (2024)
- [CodeMixBench: Evaluating Code-Mixing Capabilities of LLMs Across 18 Languages](https://mlsanity.com/paper/2507.18791/) (2025)
- [The WiLI benchmark dataset for written language identification](https://mlsanity.com/paper/1801.07779/) (2018)
- [Minimal Pair-Based Evaluation of Code-Switching](https://mlsanity.com/paper/2506.01840/) (2025)
- [ConLID: Supervised Contrastive Learning for Low-Resource Language Identification](https://mlsanity.com/paper/2506.15304/) (2025)
- [CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data](https://mlsanity.com/paper/2601.18026/) (2026)
- [OpenLID-v3: Improving the Precision of Closely Related Language Identification -- An Experience Report](https://mlsanity.com/paper/2602.13139/) (2026)
- [Mending the Holes: Mitigating Reward Hacking in Reinforcement Learning for Multilingual Translation](https://mlsanity.com/paper/2603.13045/) (2026)

## Abstract (arXiv)

Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing multiple languages. We introduce MixLID, a token-level language identification model supporting 111 language-script combinations. MixLID follows a two-stage training process: it first learns to distinguish individual languages using the language labels of monolingual examples, then continues training on mixed-language data. The second stage uses concatenated examples from existing code-switching data alongside augmented examples created through word substitution based on correspondences between English source words and loanwords. For monolingual identification, MixLID outperforms all baselines in both accuracy and macro F1 on four benchmarks within the language coverage shared with each baseline. For mixed-language identification, it outperforms the baselines in identifying the language of individual tokens on every evaluated language pair. MixLID achieves high performance across a broad range of languages in both monolingual and mixed-language settings. The model is publicly available at https://huggingface.co/nlpai-lab/MixLID.