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Tech Logic / Intelligence Frontier

Exploring the Role of Large Language Models in the Scientific Method: From Hypothesis to Discovery

Large language models (LLMs) are gradually permeating all stages of scientific research, from literature retrieval and hypothesis generation to experimental design and data analysis. Although LLMs have shown great potential in improving research productivity, their actual impact on fundamental scientific discovery remains limited. This article reviews the existing applications and future possibilities of LLMs in the scientific method, emphasizing the need to deeply integrate LLMs into the scientific process, align them with human scientific goals, and establish clear evaluation metrics.

Aug 31, 2026

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  • Large language models (LLMs) are gradually permeating all stages of scientific research, from literature retrieval and hypothesis generation to experimental design and data analysis. Although LLMs have shown great potential in improving research productivity, their actual impact on fundamental scientific discovery remains limited. This article reviews the existing applications and future possibilities of LLMs in the scientific method, emphasizing the need to deeply integrate LLMs into the scientific process, align them with human scientific goals, and establish clear evaluation metrics.
  • Tech Logic · Intelligence Frontier
  • Aug 31, 2026
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  1. 探索大型语言模型在科学方法中的作用：从假设到发现www.nature.com

How Large Language Models Are Reshaping Scientific Methods: From Hypothesis to Discovery

In recent years, the rapid development of artificial intelligence (AI) has been profoundly transforming the paradigm of scientific research. As the Nobel Prize in Physics and the Nobel Prize in Chemistry have been successively awarded to researchers in the AI field, large language models (LLMs), as representatives of generative AI, have become not merely a technical tool but also important participants in the process of scientific discovery. This article, based on a recent perspective article published in npj Artificial Intelligence, explores the application potential of LLMs across the entire scientific method workflow and the core challenges they face.

From Tools to Collaborators: The Evolving Role of LLMs in Scientific Practice

Traditional scientific research follows a rigorous methodology: posing questions, formulating hypotheses, designing experiments, collecting data, analyzing results, and drawing conclusions. In current practice, LLMs have permeated every aspect of these stages. For example, in the hypothesis generation stage, LLMs can identify interdisciplinary connections that humans might overlook by analyzing vast amounts of literature; in the experimental design stage, they can assist researchers in optimizing experimental conditions and even automatically generate experimental code; in data analysis, the natural language interaction capabilities of LLMs allow researchers to explore complex datasets in a more intuitive way.

Particularly in chemistry and biology, LLM-driven automated workflows are significantly improving research efficiency. For example, in tasks such as molecular property prediction and drug screening, LLMs not only accelerate data processing but also provide new feature representation methods. These applications indicate that LLMs are evolving from purely text-processing tools into collaborative partners embedded in scientific research workflows.

Fundamental Scientific Discovery: Opportunities and Challenges for LLMs

Although LLMs perform excellently in applied sciences, their influence in fundamental scientific fields remains limited. Fundamental science seeks to discover new principles or laws, which requires insights that go beyond existing data patterns. Current LLMs essentially learn statistical regularities from massive data; they are good at combining existing knowledge but find it difficult to truly produce "unexpected" breakthroughs. Moreover, the "black-box" nature of LLMs makes it difficult for researchers to trust the scientific conclusions they generate, especially in scenarios involving reproducibility and causal inference.

Therefore, the authors of the perspective article emphasize that LLMs should not be regarded as independent autonomous scientists, but rather positioned as "engines for augmenting human cognition." The deep integration of LLMs needs to remain aligned with human scientific goals and be accompanied by clear, quantifiable evaluation metrics to ensure that their outputs are scientifically valid and reliable.

The Road Ahead: Human-AI Collaborative Scientific Discovery

Looking ahead, LLMs are expected to play a more central role in the scientific method, but this depends on several key prerequisites. First, more transparent and interpretable AI systems need to be developed so that researchers can understand the models' reasoning logic; second, benchmarks specifically designed to evaluate the quality of scientific discoveries—not merely the fluency of text generation—need to be established; third, interdisciplinary collaboration needs to be encouraged, allowing AI researchers and domain scientists to jointly design discovery-oriented intelligent systems.

Ultimately, scientific progress has always been a product of human curiosity and creativity. LLMs can serve as a powerful lever, but the fulcrum that moves the scientific revolution remains humanity's profound inquiry into the unknown.

Conclusion

From hypothesis generation to final experimental validation and theoretical breakthroughs, large language models are gradually becoming embedded in every stage of the scientific method. Although they have not yet fully delivered on their promises in fundamental discovery, their potential cannot be overlooked. Through the deep integration of human-machine collaboration, AI and human scientists are expected to jointly usher in a new era of science that is more efficient and more creative.

We hope this article helps readers understand the current state and future of LLMs in the scientific method. It is worth noting that the content of this article is based on the original references, and readers may further consult the source texts for more details.

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