Key Takeaways & Executive Findings
- •• LLMs significantly accelerate scientific discovery by automating literature review, hypothesis generation, and experimental design. • Three main paradigms exist: knowledge extraction, hypothesis generation, and experimental planning, each with distinct strengths and limitations. • Challenges such as data quality, interpretability, and reproducibility must be addressed to ensure reliable and ethical use of LLMs in science. • Future directions include multimodal LLMs, active learning, and human-in-the-loop systems to enhance scientific reasoning and discovery.
Abstract
Large Language Models (LLMs) have emerged as powerful tools for scientific discovery, enabling researchers to accelerate hypothesis generation, experimental design, and data analysis across various domains. This survey provides a comprehensive overview of recent advances in applying LLMs to scientific research, focusing on methods for integrating domain knowledge, handling structured and unstructured data, and generating novel insights. We categorize existing approaches into three main paradigms: LLMs as knowledge extractors, LLMs as hypothesis generators, and LLMs as experimental planners. We discuss key challenges, including data quality, model interpretability, and reproducibility, and highlight promising future directions such as multimodal LLMs, active learning, and human-in-the-loop systems. Our goal is to provide a structured framework for researchers and practitioners to understand the current landscape and identify opportunities for innovation in LLM-driven scientific discovery.
1. Introduction
Large Language Models (LLMs) have revolutionized natural language processing, demonstrating remarkable capabilities in understanding and generating human-like text. Their application to scientific discovery has opened new avenues for accelerating research across disciplines, from biology and chemistry to materials science and physics. By leveraging vast amounts of scientific literature and structured data, LLMs can assist researchers in extracting knowledge, generating hypotheses, and designing experiments, thereby reducing the time and cost associated with traditional trial-and-error approaches.
This survey aims to provide a comprehensive overview of the current state of LLMs in scientific discovery. We systematically categorize existing methods into three paradigms: LLMs as knowledge extractors, which mine and synthesize information from scientific texts; LLMs as hypothesis generators, which propose novel and testable hypotheses; and LLMs as experimental planners, which design and optimize experimental protocols. We also discuss the challenges and limitations of these approaches, including issues of data quality, model interpretability, and reproducibility, and outline promising future directions that could further enhance the impact of LLMs in scientific research.
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Anonymous Authors (2026). Large Language Models for Scientific Discovery: A Survey of Methods, Applications, and Future Directions. Chinese Journal of New Drugs. https://doi.org/pub_80__articleID_202
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Frequently Asked Questions
What are the main applications of Large Language Models in scientific discovery?
LLMs are used for knowledge extraction from scientific literature, generating novel hypotheses, and designing experiments. They help automate literature review, identify patterns, and propose testable predictions, accelerating the research process.
What are the challenges of using LLMs in scientific research?
Key challenges include ensuring data quality and representativeness, maintaining model interpretability, and achieving reproducibility. Additionally, LLMs may generate plausible but incorrect outputs, requiring careful validation and human oversight.
How do LLMs generate hypotheses?
LLMs generate hypotheses by learning patterns from existing scientific knowledge and proposing new relationships or predictions. They can be fine-tuned on domain-specific data and prompted to generate hypotheses that are novel and testable.
What future directions are promising for LLMs in science?
Future directions include integrating multimodal data (e.g., text, images, and experimental data), incorporating active learning to iteratively improve models, and developing human-in-the-loop systems that combine LLM suggestions with expert feedback.
Can LLMs replace human scientists?
No, LLMs are tools to augment human capabilities, not replace them. They can handle large-scale data analysis and generate ideas, but human expertise is essential for interpreting results, ensuring validity, and making ethical decisions.
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