  Large language models (LLMs) are having transformative impacts across a wide
range of scientific fields, particularly in the biomedical sciences. Just as
the goal of Natural Language Processing is to understand sequences of words, a
major objective in biology is to understand biological sequences. Genomic
Language Models (gLMs), which are LLMs trained on DNA sequences, have the
potential to significantly advance our understanding of genomes and how DNA
elements at various scales interact to give rise to complex functions. In this
review, we showcase this potential by highlighting key applications of gLMs,
including fitness prediction, sequence design, and transfer learning. Despite
notable recent progress, however, developing effective and efficient gLMs
presents numerous challenges, especially for species with large, complex
genomes. We discuss major considerations for developing and evaluating gLMs.
