Large language models are here to stay. Here’s how I’m incorporating them, writes environmental science professor Hong Yang
In late November 2022, my university teaching was suddenly disrupted by an unexpected visitor — ChatGPT, a type of large language model algorithm that is trained on an enormous data set of language and can summarize, translate and create text.
I don’t think we can ban ChatGPT entirely, because current technology makes it difficult to detect work written by the model. My students will soon be graduating and starting jobs, and their employers might ask them to work with artificial-intelligence models. If they don’t know how to use them properly, it could hold them back. We can’t prevent students from using calculators or the Internet, and we won’t be able to prevent them from using ChatGPT.
My students study environmental science or geography, so most are not very skilled in programming languages, such as R and Python. Sometimes, I also have trouble debugging code in these languages. ChatGPT can help with that. For example, with my guidance, students used ChatGPT to create R code to conduct their statistical tests and analyse the differences in COlevels between natural and human-made sites on campus.
For example, during a lecture on greenhouse gases, I asked ChatGPT to provide a list of significant books on climate change and their authors. In an attempt to prevent any bias related to race and language, I included the phrase “regardless of race and language” in my prompt. But all of ChatGPT’s recommendations were written in English. Nine of the ten authors were white, and nine of the ten authors were men.
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