Effective language identification based on a tokenizer UnigramLM tokenizer already gives probabilities, testing those to identify a language is fast and effective. Whiceh leads me to wonder, can we identify language during training and affect behavior? arxiv.org/abs/2602.17655…
AI Weekly's analysis
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- UniLID reuses the UnigramLM tokenization algorithm to predict a string's language by asking under which language's unigram distribution the string is most likely.
- The method reaches roughly 70% accuracy with as few as five labeled samples per language in low-resource settings.
- It supports incremental addition of new languages without retraining and integrates into existing language model tokenization pipelines.
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