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Transforming science with large language models: a survey on AI-assisted scientific discovery, experimentation, content generation, and evaluation.
ArXiv e-prints, 2502.05151, February 2025. DOI: 10.48550/arXiv.2502.05151.
With the advent of large multimodal language
models, science is now at a threshold of an AI-based technological
transformation. Recently, a plethora of new AI models and tools has been
proposed, promising to empower researchers and academics worldwide to conduct
their research more effectively and efficiently. This includes all aspects of
the research cycle, especially (1) searching for relevant literature; (2)
generating research ideas and conducting experimentation; generating (3)
text-based and (4) multimodal content (e.g., scientific figures and
diagrams); and (5) AI-based automatic peer review. In this survey, we provide
an in-depth overview over these exciting recent developments, which promise
to fundamentally alter the scientific research process for good. Our survey
covers the five aspects outlined above, indicating relevant datasets, methods
and results (including evaluation) as well as limitations and scope for
future research. Ethical concerns regarding shortcomings of these tools and
potential for misuse (fake science, plagiarism, harms to research integrity)
take a particularly prominent place in our discussion. We hope that our
survey will not only become a reference guide for newcomers to the field but
also a catalyst for new AI-based initiatives in the area of
“AI4Science”.
@article{eger2025transforming,
author = {Steffen Eger and Yong Cao and Jennifer D'Souza and
Andreas Geiger and Christian Greisinger and Stephanie Gross and Yufang Hou
and Brigitte Krenn and Anne Lauscher and Yizhi Li and Chenghua Lin and Nafise
Sadat Moosavi and Wei Zhao and Tristan Miller},
title = {Transforming Science with Large Language Models: a
Survey on {AI}-assisted Scientific Discovery, Experimentation, Content
Generation, and Evaluation},
journal = {{ArXiv} e-prints},
volume = {2502.05151},
month = feb,
year = {2025},
doi = {10.48550/arXiv.2502.05151},
}