The impact of tokenizer selection in genomic language models.

ABSTRACT:

Motivation

Genomic language models have recently emerged as a new method to decode, interpret, and generate genetic sequences. Existing genomic language models have utilized various tokenization methods, including character tokenization, overlapping and nonoverlapping k-mer tokenization, and byte-pair encoding, a method widely used in natural language models. Genomic sequences differ from natural language because of their low character variability, complex and overlapping features, and inconsistent directionality. These features make subword tokenization in genomic language models significantly different from both traditional language models and protein language models.

Results

This study explores the impact of tokenization in genomic language models by evaluating their down

SUBMITTER: Lindsey LM 

PROVIDER: S-EPMC12453675 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature