An Interdisciplinary Outlook on Large Language Models for Scientific
  Research
James Boyko Joseph Cohen Nathan Fox Maria Han Veiga Jennifer I-Hsiu Li Jing Liu Bernardo Modenesi Andreas H. Rauch Kenneth N. Reid Soumi Tribedi Anastasia Visheratina Xin Xie
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.