An AI algorithm helped researchers generate scientific hypotheses about astronomy that were almost as good as experts - by AndyTomaswick
To avoid that problem, the researchers, led by Ioana Ciuc? and Yuan-Sen Ting of ANU, used a piece of code known as an application programming interface , which was written in Python, known as Langchain. This API allows more advanced users to manipulate LLMs like GPT-4, which serves as the latest basis for ChatGPT. In the researchers’ case, they loaded over 1,000 scientific articles relating to Galactic Astronomy into GPT-4 after downloading them from NASA’s Astrophysics Data System.
One of the researchers’ experiments was to test how the number of papers the model had access to affected its resulting hypotheses. They noticed a significant difference between the suggested hypotheses it developed having access to only ten papers vs. having access to the full thousand. But how did they judge the validity of the hypotheses themselves? They did what any self-respecting scientist would do and recruited experts in the field. Two of them, to be precise. And they asked them to just the hypotheses based on originality of thought, the feasibility of testing the hypotheses, and the scientific accuracy of its basis.
A critical factor in determining the final hypotheses that were presented to the experts was that the hypotheses were refined using “adversarial prompting.
Even with the adversarial feedback, there’s no reason for astronomy Ph.D. students to give up on coming up with their own unique ideas in their field. But, this study does point to an underutilized ability of these LLMs. As they become more widely adopted, scientists and laypeople can leverage them more and more to come up with new and better ideas to test.
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