大型语言模型(LLM)正在加速科学理论研究，尤其是在缺乏连贯理论来连接实验数据的领域。LLM能够快速应用原理、提出批评，并提供跨领域见解，弥合理论差距。对于自学者，LLM能模拟领域专家的观点，帮助他们应对挑战。LLM的出现将促进跨学科研究，使科学家更容易在不同领域之间取得进展。研究人员可以通过LLM模拟辩论，或引入哲学视角，从而获得对研究问题的全新理解。现在是利用LLM进行跨学科研究的黄金时代。

💡LLM通过快速应用原理、提出批评，并提供跨领域见解，极大地加速了科学理论研究，尤其是在缺乏连贯理论来连接实验数据的领域。

🧑‍🎓LLM解决了自学者在现有科学领域中遇到的关键问题，能够模拟领域专家的观点，帮助他们应对挑战，并预测正统研究人员的批评。

🍎LLM的出现将促进跨学科研究，使科学家更容易在不同领域之间取得进展，摘取以前难以触及的“低垂果实”。

🗣️研究人员可以利用LLM模拟不同研究方法论的专家之间的辩论，或者引入哲学视角，从而获得对研究问题的全新理解。

📚在跨学科研究中，尚不明确利用LLM的最佳方法，但目前存在大量有待挖掘的潜力，现在是利用LLM进行跨学科研究的黄金时代。

Published on May 7, 2025 7:48 PM GMT

I'm right now doing conceptual theoretical work about how the human fascia system works. While I do rely on some original conceptual insights that I have e come up with on my own, Gemini 2.5 Pro massively speeds up my conceptual work. Being able to formulate a principle or analogy and then having Gemini apply it, is very useful.

There are a bunch of scientific fields where we currently have a lot of experimental data but lack coherent theory to interlink the experimental findings. Based on my own experience, current LLMs seem already to be powerful enough to help bridge that theory gap. Being able to ask "Hey, does field XYZ have any useful insights to the problem I'm tackling?" is also very helpful for making progress in theory.

The LLMs also solve a key problems that autodidact have when it comes with existing scientific fields. If you have a new idea, they are good at telling you the main criticisms that would come from an orthodox researcher in a field. We might see a rise in interdisciplinary work that didn't happen in the past because of academia's hyperspecialization. 

People frequently say that progress in science has stalled because there's little low-hanging fruit. When it comes to doing certain interdisciplinary work, it's now a lot easier to pick the fruit. If you are right now starting a scientific career, think about what kind of interdisciplinary work you might do, where it's now easier to make progress because of the existence of LLMs. 

If you have a research question, one approach you can do is to ask a reasoning model to create a debate between two highly skilled researchers with different approaches to debate your research question. You might learn valuable insights about your research question this way. Besides taking existing researchers in the field, asking the LLM to simulate philosophers and tell the LLM that the philosophers understand all the facts about a field, might give you valuable insights of how insights that philosophers found through a lot of hard work translate into individual fields. 

It's not clear what the best approaches are to get the LLM to help you with interdisciplinary work, but there's a lot of fruit out there to be picked right now.

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