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In this paper, we describe the capabilities and constraints of Large LanguageModels (LLMs) within disparate academic disciplines, aiming to delineate theirstrengths and limitations with precision. We examine how LLMs augmentscientific inquiry, offering concrete examples such as accelerating literaturereview by summarizing vast numbers of publications, enhancing code developmentthrough automated syntax correction, and refining the scientific writingprocess. Simultaneously, we articulate the challenges LLMs face, includingtheir reliance on extensive and sometimes biased datasets, and the potentialethical dilemmas stemming from their use. Our critical discussion extends tothe varying impacts of LLMs across fields, from the natural sciences, wherethey help model complex biological sequences, to the social sciences, wherethey can parse large-scale qualitative data. We conclude by offering a nuancedperspective on how LLMs can be both a boon and a boundary to scientificprogress.

An Interdisciplinary Outlook on Large Language Models for Scientific Research