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

2025-07-15

AI Summary

Scientific innovation faces challenges including information overload, disciplinary silos, and diminishing returns from conventional methodologies; yet existing literature lacks a systematic characterization of large language models’ (LLMs) evolving roles in research. This paper proposes a three-tiered framework for LLMs in scientific innovation: (1) *Evaluator*—verifying and filtering hypotheses; (2) *Collaborator*—assisting in modeling and scientific writing; and (3) *Scientist*—autonomously generating hypotheses and designing experiments—while explicitly distinguishing *structured research* from *open-ended discovery*. Through integrated methodology analysis, benchmarking, system architecture design, and multi-dimensional evaluation metrics, we delineate capability boundaries and human-AI collaboration paradigms across tiers. Our work constitutes the first systematic survey on LLM-driven scientific innovation, accompanied by an open-source resource repository—including a conceptual framework, practical tools, and evaluation standards—to advance AI-augmented scientific discovery and stimulate ethical reflection. (149 words)

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Understanding LLMs' transformative roles in scientific innovation
• Differentiating LLM contributions in structured vs open-ended science
• Clarifying capability boundaries and human-AI interaction patterns

Innovation

• LLMs categorized as Evaluator, Collaborator, Scientist
• Framework clarifies capability boundaries, interaction patterns
• LLMs as catalysts reshaping scientific foundations