科学大型语言模型：生物与化学领域综述
Scientific Large Language Models: A Survey on Biological & Chemical Domains
AI总结：
本文系统综述了面向生物与化学领域的科学大型语言模型，涵盖文本知识、小分子、蛋白质及基因序列等模态的架构、数据集与评估，并指出了当前挑战与未来研究方向。
摘要：
大型语言模型（LLMs）在增强自然语言理解方面展现出变革性力量，代表了向通用人工智能迈出的重要一步。LLMs的应用超越了传统语言边界，涵盖了各科学学科中开发的专门语言系统。这种日益增长的兴趣催生了科学LLMs，这是一类专为促进科学发现而设计的新型子类。作为AI for Science社区中新兴领域，科学LLMs值得全面探索。然而，目前缺乏系统且最新的综述介绍它们。本文系统界定“科学语言”概念，同时全面回顾科学LLMs最新进展。鉴于科学学科领域广泛，本文分析聚焦于生物和化学领域。这包括深入考察文本知识、小分子、大分子蛋白质、基因组序列及其组合的LLMs，从模型架构、能力、数据集和评估方面进行分析。最后，批判性审视当前挑战，指出LLMs发展前景研究方向。通过提供该领域技术发展全面概述，本综述期望成为研究人员探索科学LLMs复杂领域的宝贵资源。
英文摘要：
Large Language Models (LLMs) have emerged as a transformative power in enhancing natural language comprehension, representing a significant stride toward artificial general intelligence. The application of LLMs extends beyond conventional linguistic boundaries, encompassing specialized linguistic systems developed within various scientific disciplines. This growing interest has led to the advent of scientific LLMs, a novel subclass specifically engineered for facilitating scientific discovery. As a burgeoning area in the community of AI for Science, scientific LLMs warrant comprehensive exploration. However, a systematic and up-to-date survey introducing them is currently lacking. In this paper, we endeavor to methodically delineate the concept of "scientific language", whilst providing a thorough review of the latest advancements in scientific LLMs. Given the expansive realm of scientific disciplines, our analysis adopts a focused lens, concentrating on the biological and chemical domains. This includes an in-depth examination of LLMs for textual knowledge, small molecules, macromolecular proteins, genomic sequences, and their combinations, analyzing them in terms of model architectures, capabilities, datasets, and evaluation. Finally, we critically examine the prevailing challenges and point out promising research directions along with the advances of LLMs. By offering a comprehensive overview of technical developments in this field, this survey aspires to be an invaluable resource for researchers navigating the intricate landscape of scientific LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
