Empowering scientific discovery with explainable small domain-specific and large language models

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Dades bibliogràfiques
Publicat a:	The Artificial Intelligence Review vol. 58, no. 12 (Dec 2025), p. 371
Autor principal:	Yu, Hengjie
Altres autors:	Wang, Yizhi, Cheng, Tao, Yan, Yan, Dawson, Kenneth A., Li, Sam F. Y., Zheng, Yefeng, Jin, Yaochu
Publicat:	Springer Nature B.V.
Matèries:	Problem solving Reliability Knowledge Classification Discovery Collaboration Research Methodological problems Models Taxonomy Adoption of innovations Prompt engineering Laboratories Artificial intelligence Validity Hypotheses Innovations Medical research Paradigms Global local relationship Agent-based models Scientists Agnosticism Retrieval Knowledge management Researchers Automation Explainable artificial intelligence Large language models Empowerment Language modeling
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Resum:	As artificial intelligence (AI) increasingly integrates into scientific research, explainability has become a cornerstone for ensuring reliability and innovation in discovery processes. This review offers a forward-looking integration of explainable AI (XAI)-based research paradigms, encompassing small domain-specific models, large language models (LLMs), and agent-based large-small model collaboration. For domain-specific models, we introduce a knowledge-oriented taxonomy categorizing methods into knowledge-agnostic, knowledge-based, knowledge-infused, and knowledge-verified approaches, emphasizing the balance between domain knowledge and innovative insights. For LLMs, we examine three strategies for integrating domain knowledge—prompt engineering, retrieval-augmented generation, and supervised fine-tuning—along with advances in explainability, including local, global, and conversation-based explanations. We also envision future agent-based model collaborations within automated laboratories, stressing the need for context-aware explanations tailored to research goals. Additionally, we discuss the unique characteristics and limitations of both explainable small domain-specific models and LLMs in the realm of scientific discovery. Finally, we highlight methodological challenges, potential pitfalls, and the necessity of rigorous validation to ensure XAI’s transformative role in accelerating scientific discovery and reshaping research paradigms.
ISSN:	0269-2821 1573-7462
DOI:	10.1007/s10462-025-11365-w
Font:	ABI/INFORM Global