Genomic (DNA) sequences encode an enormous amount of information for gene
regulation and protein synthesis. Similar to natural language models,
researchers have proposed foundation models in genomics to learn generalizable
features from unlabeled genome data that can then be fine-tuned for downstream
tasks such as identifying regulatory elements. Due to the quadratic scaling of
attention, previous Transformer-based genomic models have used 512 to 4k tokens
as context (<0.001% of the human genome), significantly limiting the modeling
of long-range interactions in DNA. In addition, these methods rely on
tokenizers to aggregate meaningful DNA units, losing single nucleotide
resolution where subtle genetic variations can completely alter protein
function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large
language model based on implicit convolutions was shown to match attention in
quality while allowing longer context lengths and lower time complexity.
Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic
foundation model pretrained on the human reference genome with context lengths
of up to 1 million tokens at the single nucleotide-level, an up to 500x
increase over previous dense attention-based models. HyenaDNA scales
sub-quadratically in sequence length (training up to 160x faster than
Transformer), uses single nucleotide tokens, and has full global context at
each layer. We explore what longer context enables - including the first use of
in-context learning in genomics for simple adaptation to novel tasks without
updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide
Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets
using a model with orders of magnitude less parameters and pretraining data. On
the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by
+9 accuracy points.

HyenaDNA 是一个在人类参考基因组上进行预训练的基因组基础模型，具有长达 100 万个单核苷酸的上下文长度和单核苷酸层面的全局上下文，以及可用于简单调整以适应新任务而无需更新预训练模型权重的上下文学习能力，并在核苷酸转换器和基因组基准测试中达到了最先进的水平。