Authors: Steffen Eger, Yong Cao, Jennifer D’Souza, Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Sadat Moosavi, Wei Zhao, Tristan Miller
Abstract: 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”.
Source: http://arxiv.org/abs/2502.05151v1
