What Language is This? Ask Your Tokenizer
arXiv:2602.17655v1 Announce Type: new Abstract: Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus curation, training data analysis, and cross-lingual evaluation of large language models. Despite near-perfect performance on high-resource languages, existing systems remain brittle in low-resource and closely related language settings. We introduce UniLID, a simple and efficient LID method based on the UnigramLM tokenization algorithm, leveraging its probabilistic framing, parameter estimation technique and inference strategy. In short, we learn language-conditional unigram distributions over a shared tokenizer vocabulary but treat segmentation as a language-specific phenomenon. Our formulation is data- and compute-efficient, supports incremental addition of new languages without retraining existing models, and can naturally be integrated into existing language model tokenization pipelines. Empi
arXiv:2602.17655v1 Announce Type: new Abstract: Language Identification (LID) is an important component of many multilingual natural language processing pipelines, where it facilitates corpus curation, training data analysis, and cross-lingual evaluation of large language models. Despite near-perfect performance on high-resource languages, existing systems remain brittle in low-resource and closely related language settings. We introduce UniLID, a simple and efficient LID method based on the UnigramLM tokenization algorithm, leveraging its probabilistic framing, parameter estimation technique and inference strategy. In short, we learn language-conditional unigram distributions over a shared tokenizer vocabulary but treat segmentation as a language-specific phenomenon. Our formulation is data- and compute-efficient, supports incremental addition of new languages without retraining existing models, and can naturally be integrated into existing language model tokenization pipelines. Empirical evaluations against widely used baselines, including fastText, GlotLID, and CLD3, show that UniLID achieves competitive performance on standard benchmarks, substantially improves sample efficiency in low-resource settings - surpassing 70% accuracy with as few as five labeled samples per language - and delivers large gains on fine-grained dialect identification.
Executive Summary
This article introduces UniLID, a simple and efficient Language Identification (LID) method that leverages the UnigramLM tokenization algorithm to improve performance on low-resource languages and closely related language settings. The proposed formulation is data- and compute-efficient, supports incremental addition of new languages, and can be integrated into existing language model tokenization pipelines. Empirical evaluations show that UniLID achieves competitive performance on standard benchmarks, substantially improves sample efficiency, and delivers large gains on fine-grained dialect identification. While the results are promising, the article highlights the need for further research to address the limitations of the approach. The method's potential applications in multilingual natural language processing pipelines and its ability to improve language model robustness make it a valuable contribution to the field.
Key Points
- ▸ UniLID is a simple and efficient LID method based on UnigramLM tokenization algorithm
- ▸ The method is data- and compute-efficient, and supports incremental addition of new languages
- ▸ UniLID achieves competitive performance on standard benchmarks and substantially improves sample efficiency
Merits
Improves Performance on Low-Resource Languages
UniLID's ability to learn language-conditional unigram distributions and treat segmentation as a language-specific phenomenon enables it to perform well on low-resource languages
Supports Incremental Addition of New Languages
UniLID's incremental learning approach allows for the addition of new languages without retraining existing models
Can be Integrated into Existing Language Model Tokenization Pipelines
UniLID's formulation can be naturally integrated into existing language model tokenization pipelines
Demerits
Limited to Tokenization-Based Approach
UniLID's reliance on tokenization-based approach may limit its ability to capture more complex linguistic phenomena
Requires Large Amount of Training Data
While UniLID is data-efficient, it still requires a large amount of training data to achieve optimal performance
Expert Commentary
While UniLID is a promising contribution to the field of language identification, its limitations and potential applications must be carefully considered. The article highlights the need for further research to address the limitations of the approach and to explore its scalability to more languages. Additionally, the article raises important questions about the potential impact of UniLID on language model deployment in real-world applications. Experts in the field should carefully evaluate the results and implications of UniLID and consider its potential applications and limitations.
Recommendations
- ✓ Further research should be conducted to address the limitations of UniLID and to explore its scalability to more languages
- ✓ UniLID should be evaluated in real-world applications to assess its practical implications and potential impact on language model deployment
