The Human Genomics Long-Range Benchmark: Advancing DNA Language Models

ICLR 2026 Conference Submission13855 Authors

Keywords: Language Models, DNA, DNA LMs, Benchmark

TL;DR: A biologically meaningful benchmark to evaluate DNA Language Models with a focus on long-range interactions.

Abstract: The advent of language models (LMs) in genomics necessitates benchmarks that can assess models’ capabilities and limitations. In contrast to protein models, DNA LMs can be used to study non-coding regions of the genome and must account for unique challenges, especially interactions across long sequence lengths. However, existing benchmarks for DNA LMs are defined over short sequence datasets and can involve tasks that are not considered to be biologically meaningful. Here, we present the Human Genomics Long-Range Benchmark (LRB), which focuses on biologically meaningful tasks and supports long-range contexts. We complement our benchmark with fine-tuning recipes that meaningfully improve performance. We evaluate DNA LMs across nine compiled human genome tasks and observe that they achieve competitive performance relative to supervised baselines on several tasks (e.g., genome annotation), but there remains a significant gap in domains, such as variant effect and gene expression prediction. Additionally, we introduce a visualization tool to examine model performance split by genomic properties.

Primary Area: datasets and benchmarks

Submission Number: 13855