28 lines
2.0 KiB
JSON
28 lines
2.0 KiB
JSON
{
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"title": "Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation",
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"authors": [
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"Steffen Eger",
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"Yong Cao",
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"Jennifer D'Souza",
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"Andreas Geiger",
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"Christian Greisinger",
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"Stephanie Gross",
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"Yufang Hou",
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"Brigitte Krenn",
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"Anne Lauscher",
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"Yizhi Li",
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"Chenghua Lin",
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"Nafise Sadat Moosavi",
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"Wei Zhao",
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"Tristan Miller"
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],
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"abstract": "With the advent of large multimodal language models, science is now at a\nthreshold of an AI-based technological transformation. Recently, a plethora of\nnew AI models and tools has been proposed, promising to empower researchers and\nacademics worldwide to conduct their research more effectively and efficiently.\nThis includes all aspects of the research cycle, especially (1) searching for\nrelevant literature; (2) generating research ideas and conducting\nexperimentation; generating (3) text-based and (4) multimodal content (e.g.,\nscientific figures and diagrams); and (5) AI-based automatic peer review. In\nthis survey, we provide an in-depth overview over these exciting recent\ndevelopments, which promise to fundamentally alter the scientific research\nprocess for good. Our survey covers the five aspects outlined above, indicating\nrelevant datasets, methods and results (including evaluation) as well as\nlimitations and scope for future research. Ethical concerns regarding\nshortcomings of these tools and potential for misuse (fake science, plagiarism,\nharms to research integrity) take a particularly prominent place in our\ndiscussion. We hope that our survey will not only become a reference guide for\nnewcomers to the field but also a catalyst for new AI-based initiatives in the\narea of \"AI4Science\".",
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"pdf_url": "http://arxiv.org/pdf/2502.05151v1",
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"entry_id": "http://arxiv.org/abs/2502.05151v1",
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"categories": [
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"cs.CL",
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"cs.AI",
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"cs.CV",
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"cs.LG"
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]
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} |