Unigram Tokenization: How Probability Determines Subword Choices

Unigram Tokenization is an alternative vocabulary construction method widely used in NLP systems that prioritize flexible token selection and probabilistic modeling. Unlike BPE, which builds a vocabulary through iterative merges, the Unigram approach begins with a large candidate vocabulary and gradually removes items based on statistical likelihood. This top-down design gives Unigram tokenizers a unique ability to generate multiple valid segmentations for a single word.

At the heart of the Unigram model is a probabilistic framework. Each subword unit is assigned a score based on how likely it is to appear across many segmentations. During training, the algorithm evaluates millions of potential splits and identifies the most cost-effective ones according to a predefined loss function. Subwords that consistently perform poorly or provide minimal representational benefit are removed from the candidate set.

This process results in a refined vocabulary where each token has a meaningful statistical justification. Because of this, Unigram Tokenization is more adaptable when dealing with languages that have complex morphology or lack clear word boundaries. For example, languages like Japanese or Finnish benefit greatly from the multiple segmentation paths that Unigram models can generate.

In practice, a Unigram tokenizer does not simply apply a deterministic set of rules. Instead, it produces a set of possible segmentations and then chooses the most probable one during inference. This allows for greater flexibility and often results in more compact and intuitive representations than purely rule-based strategies.

Despite its strengths, Unigram Tokenization can be computationally heavier to train due to the large candidate vocabularies it must evaluate. However, its performance benefits at inference time—combined with cleaner generalization for many languages—make it a strong option for multilingual and morphologically diverse datasets.

For developers who want more natural segmentation and deeper linguistic representation, the Unigram model provides a powerful tool. Understanding how probability drives subword selection helps explain why some tokenizers behave differently across datasets or languages, even when handling the same input text.