2026/04/07 by Weicai Long, Yusen Hou, Junning Feng +5 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · #Benchmark (surveying) #Benchmarking #Biomedical Text Mining and Ontologies #DNA binding site #DNA sequencing #Exploit #Genomics #Genomics and Rare Diseases #Inference #Machine Learning in Bioinformatics #Sequence (biology) #Sequence motif #cs.CL #q-bio.GN
paper · pdf · doi:10.48550/arxiv.2604.05774
openalex publication_date 2026/04/07 · arxiv published 2026/04/07 · arxiv updated 2026/04/07 · openalex created_date 2026/04/09 · openalex updated_date 2026/07/28
Large Language Models (LLMs) are increasingly adopted as conversational assistants in genomics, where they are mainly used to reason over biological knowledge, annotations, and analysis outputs through natural language interfaces. However, existing benchmarks either focus on specialized DNA models trained for sequence prediction or evaluate biological knowledge using text-only questions, leaving the behavior of general-purpose LLMs when directly exposed to raw genome sequences underexplored. We introduce GenomeQA, a benchmark designed to provide a controlled evaluation setting for general-purpose LLMs on sequence-based genome inference tasks. GenomeQA comprises 5,200 samples drawn from multiple biological databases, with sequence lengths ranging from 6 to 1,000 base pairs (bp), spanning six task families: Enhancer and Promoter Identification, Splice Site Identification, Taxonomic Classification, Histone Mark Prediction, Transcription Factor Binding Site Prediction, and TF Motif Prediction. Across six frontier LLMs, we find that models consistently outperform random baselines and can exploit local sequence signals such as GC content and short motifs, while performance degrades on tasks that require more indirect or multi-step inference over sequence patterns. GenomeQA establishes a diagnostic benchmark for studying and improving the use of general-purpose LLMs on raw genomic sequences.