In this paper, we describe the capabilities and constraints of Large Language Models (LLMs) within disparate academic disciplines, aiming to delineate their strengths and limitations with precision. We examine how LLMs augment scientific inquiry, offering concrete examples such as accelerating literature review by summarizing vast numbers of publications, enhancing code development through automated syntax correction, and refining the scientific writing process. Simultaneously, we articulate the challenges LLMs face, including their reliance on extensive and sometimes biased datasets, and the potential ethical dilemmas stemming from their use. Our critical discussion extends to the varying impacts of LLMs across fields, from the natural sciences, where they help model complex biological sequences, to the social sciences, where they can parse large-scale qualitative data. We conclude by offering a nuanced perspective on how LLMs can be both a boon and a boundary to scientific progress.

翻译：本文系统阐述了大型语言模型在不同学科领域中的能力与局限，旨在精准界定其优势与不足。我们考察了大语言模型如何提升科学研究：通过总结海量文献加速文献综述、借助自动化语法纠错提升代码开发效率、完善科学写作流程等具体案例。同时，我们揭示了这类模型面临的挑战，包括对大规模、有时存在偏差数据集的依赖，以及其应用引发的潜在伦理困境。我们的批判性讨论进一步延伸至大语言模型在各领域的差异化影响——从帮助模拟复杂生物序列的自然科学，到解析大规模质性数据的社会科学。最终，我们提出一个平衡视角，阐明大语言模型如何既是科学进步的助推器，亦是其边界所在。