  Although DNA foundation models have advanced the understanding of genomes,
they still face significant challenges in the limited scale and diversity of
genomic data. This limitation starkly contrasts with the success of natural
language foundation models, which thrive on substantially larger scales.
Furthermore, genome understanding involves numerous downstream genome
annotation tasks with inherent data heterogeneity, thereby necessitating more
efficient and robust fine-tuning methods tailored for genomics. Here, we
present \textsc{Lingo}: \textsc{L}anguage prefix f\textsc{In}e-tuning for
\textsc{G}en\textsc{O}mes. Unlike DNA foundation models, \textsc{Lingo}
strategically leverages natural language foundation models' contextual cues,
recalibrating their linguistic knowledge to genomic sequences. \textsc{Lingo}
further accommodates numerous, heterogeneous downstream fine-tune tasks by an
adaptive rank sampling method that prunes and stochastically reintroduces
pruned singular vectors within small computational budgets. Adaptive rank
sampling outperformed existing fine-tuning methods on all benchmarked 14 genome
understanding tasks, while requiring fewer than 2\% of trainable parameters as
genomic-specific adapters. Impressively, applying these adapters on natural
language foundation models matched or even exceeded the performance of DNA
foundation models. \textsc{Lingo} presents a new paradigm of efficient and
scalable genome understanding via genomic-specific adapters on language models.
