Скачать или смотреть Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery

This paper discusses how Large Language Models (LLMs) are significantly changing scientific research by boosting productivity and altering the traditional scientific method. LLMs are increasingly being used to assist with tasks across the scientific process, including designing experiments, analyzing data, and managing workflows, particularly in fields like chemistry and biology. The authors highlight LLMs' potential to aid in hypothesis generation, experimental design, and observation analysis. The emergence of "foundation models," which are large models trained on extensive and diverse scientific data, represents a significant step, as they can encapsulate broad domain knowledge and be adapted for various scientific applications. However, the paper also points out key challenges such as LLMs generating false information (hallucinations), struggling with complex reasoning, and lacking transparency. While hallucinations pose risks, the authors suggest they might also be a potential source for generating novel hypotheses. Ultimately, the paper concludes that for LLMs to evolve into "creative engines" capable of fundamental scientific discovery, they need to be deeply integrated into the scientific process, working collaboratively with human scientists who provide essential guidance, validation, and oversight.

https://arxiv.org/pdf/2505.16477