DNA Language Models: An Assessment of Pre-Training for Fine-Tuning Tasks
Recent breakthroughs in foundation models and Large Language Models (LLMs) have introduced new opportunities for studying and decoding genomic sequences. Several state-of-the-art approaches, such as DNABERT2, rely on transformer-based architectures, while others, such as ConvNova, still build upon more conventional convolutional models. However, systematic benchmark comparisons across these methods remain scarce. Given that transformer-based models require extensive and costly pretraining, it is crucial to evaluate whether their performance gains justify this overhead. Moreover, LLMs such as DNABERT2 typically rely on Byte Pair Encoding (BPE) tokenization, whose relevance for DNA sequence...
| Authors | Romain Karpinsky, Julien Mozziconacci, Mickaël Delcey |
|---|---|
| Primary category | q-bio.GN |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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