The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist

• URL: http://arxiv.org/abs/2507.11810v1
• Date: Wed, 16 Jul 2025 00:11:01 GMT
• Title: The Evolving Role of Large Language Models in Scientific Innovation: Evaluator, Collaborator, and Scientist
• Authors: Haoxuan Zhang, Ruochi Li, Yang Zhang, Ting Xiao, Jiangping Chen, Junhua Ding, Haihua Chen,
• Abstract summary: Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs)<n>This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist.
• Score: 3.7803247326675162
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific innovation is undergoing a paradigm shift driven by the rapid advancement of Large Language Models (LLMs). As science faces mounting challenges including information overload, disciplinary silos, and diminishing returns on conventional research methods, LLMs are emerging as powerful agents capable not only of enhancing scientific workflows but also of participating in and potentially leading the innovation process. Existing surveys mainly focus on different perspectives, phrases, and tasks in scientific research and discovery, while they have limitations in understanding the transformative potential and role differentiation of LLM. This survey proposes a comprehensive framework to categorize the evolving roles of LLMs in scientific innovation across three hierarchical levels: Evaluator, Collaborator, and Scientist. We distinguish between LLMs' contributions to structured scientific research processes and open-ended scientific discovery, thereby offering a unified taxonomy that clarifies capability boundaries, evaluation criteria, and human-AI interaction patterns at each level. Through an extensive analysis of current methodologies, benchmarks, systems, and evaluation metrics, this survey delivers an in-depth and systematic synthesis on LLM-driven scientific innovation. We present LLMs not only as tools for automating existing processes, but also as catalysts capable of reshaping the epistemological foundations of science itself. This survey offers conceptual clarity, practical guidance, and theoretical foundations for future research, while also highlighting open challenges and ethical considerations in the pursuit of increasingly autonomous AI-driven science. Resources related to this survey can be accessed on GitHub at: https://github.com/haoxuan-unt2024/llm4innovation.

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