In many scientific fields, large language models (LLMs) have revolutionized
the way with which text and other modalities of data (e.g., molecules and
proteins) are dealt, achieving superior performance in various applications and
augmenting the scientific discovery process. Nevertheless, previous surveys on
scientific LLMs often concentrate on one to two fields or a single modality. In
this paper, we aim to provide a more holistic view of the research landscape by
unveiling cross-field and cross-modal connections between scientific LLMs
regarding their architectures and pre-training techniques. To this end, we
comprehensively survey over 250 scientific LLMs, discuss their commonalities
and differences, as well as summarize pre-training datasets and evaluation
tasks for each field and modality. Moreover, we investigate how LLMs have been
deployed to benefit scientific discovery. Resources related to this survey are
available at this https URL

该研究对 250 个科学领域的大型语言模型进行了全面调研，揭示了它们在架构和预训练技术上的交叉领域和跨模态连接，并总结了每个领域和模态的预训练数据集和评估任务。此外，还研究了大型语言模型在科学发现中的应用。