首个面向多组学序列的大规模指令微调数据集，提升大模型生物理解能力。
Biology-Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
- 构建包含DNA/RNA/蛋白质的多组学序列指令数据集
- 现有大模型在未微调下对多组学任务表现不佳
- 提出三阶段训练框架，显著增强模型生物学推理能力
大语言模型在通用领域表现出色，但在多组学生物学应用中仍鲜有探索。为此，我们推出Biology-Instructions，首个面向多组学生物序列（包括DNA、RNA、蛋白质及多分子）的大规模指令微调数据集。该数据集连接大语言模型与复杂生物序列任务，提升其泛化性与推理能力，同时保持对话流畅性。我们还揭示了当前顶尖大模型在缺乏专门训练时，在多组学任务上的显著局限。为克服此问题，我们提出ChatMultiOmics，一种基于新型三阶段训练流程的强基线模型，通过Biology-Instructions验证其在多组学理解上的优越性能。两项资源均公开可用，推动大模型在多组学分析中的深度融合。Biology-Instructions可访问：https://github.com/hhnqqq/Biology-Instructions。
原文摘要 · Abstract (English)
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.
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