AI Tools: ChatGPT, Copilot, and Large Language Models

Large Language Models (LLM)

LLMs are neural network models focused on language, trained with large datasets, and designed for natural language processing. LLMs are designed to model human language and use mathematical models to predict what the next word is most likely to be based on what you are asking for.

Generative Pre-trained Transformers (ChatGPT, Copilot)

GPTs are artificial neural networks that are used in natural language processing by machines. They are pre-trained on large data sets of text.

ChatGPT is a generative pre-trained artificial intelligence chatbot developed by OpenAI. Copilot is a Microsoft LLM developed to be integrated into other Microsoft applications. ChatGPT and Copilot are artificial intelligence language models, similar to a chatbot, but more robust. ChatGPT and Copilot are not a search engine where you are given a set of results to a specific search, but instead create “new content” by predicting the word most likely to come next based on use of a dataset of publicly available Internet sites.

Keep in mind: these bots don't think. They don't understand, read, choose or give you the "best information." Sometimes it might feel or seem like it, but this isn't how the technology works. They also won't tell you where they got the information they're pulling from, and who is doing the work behind the scenes. Many, if not most, are unregulated and influenced by how we all interact with it.

One of the most common ways users interact with ChatGPT or Copilot is to ask a question or give a prompt, and then quickly get an answer. Generative AI tools model human language and use mathematical models to predict what the next word is most likely to be based on your question. They are trained on very, very large datasets of information, like publicly available internet sites. This means AI tools can inherit inaccuracies and biases present in these datasets.

References

University of Minnesota Libraries. (2025, January 22). ChatGPT and other AI tools. https://libguides.umn.edu/c.php?g=1314591&p=9664664

Bowen, J.A. & Watson, C.E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

University of Minnesota Libraries. (2023, July 21). ChatGPT and other AI tools. https://libguides.umn.edu/c.php?g=1314591&p=9664664

What information does a LLM use?

LLMs are trained on a body of text which allows it to generate text in response to a prompt.  For example, some partial lists of the ChatGPT training dataset exist, and ChatGPT will also provide a partial list when queried. However, the entire body of text that has trained ChatGPT is unknown.

When an LLM provides an answer to a question, it will not immediately provide a reference for where the information came from. This is because it is pulling predictive language from a wide variety of places, so the information usually doesn't come from a single source. This means a user typically cannot trace the response back to a single parent source or know where the information came from.

References

Scheelke, A. (2023, July 10). AI, ChatGPT, and the Library. https://libguides.slcc.edu/ChatGPT/InformationLiteracy