mlsanityData to 7 Oct 2026

Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models

arXiv 2412.19191 ↗First posted 26 Dec 2024q-bio.BM cs.AI cs.LGcited by 9

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Abstract (arXiv)

Large language models (LLMs) have shown remarkable capabilities in general domains, but their application to multi-omics biology remains underexplored. To address this gap, we introduce Biology-Instructions, the first large-scale instruction-tuning dataset for multi-omics biological sequences, including DNA, RNA, proteins, and multi-molecules. This dataset bridges LLMs and complex biological sequence-related tasks, enhancing their versatility and reasoning while maintaining conversational fluency. We also highlight significant limitations of current state-of-the-art LLMs on multi-omics tasks without specialized training. To overcome this, we propose ChatMultiOmics, a strong baseline with a novel three-stage training pipeline, demonstrating superior biological understanding through Biology-Instructions. Both resources are publicly available, paving the way for better integration of LLMs in multi-omics analysis. The Biology-Instructions is publicly available at: https://github.com/hhnqqq/Biology-Instructions.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 32 of its 86 references that are in the map. First posted on arXiv 26 Dec 2024.

Builds on

32 of 86 references.

References · 32

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20 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 7.

9 papers.