IBM describes Large Language Models as "a category of foundation models trained on immense amounts of data making them capable of understanding and generating natural language and other types of content to perform a wide range of tasks". As they discuss in their article What are large language models (LLMs)?, LLMs have been in use for some time but have become more mainstream and accessible to the public with Open AI's Chat GPT.
Technical jargon aside, IBM puts it best by saying "In a nutshell, LLMs are designed to understand and generate text like a human, in addition to other forms of content, based on the vast amount of data used to train them. They have the ability to infer from context, generate coherent and contextually relevant responses, translate to languages other than English, summarize text, answer questions (general conversation and FAQs) and even assist in creative writing or code generation tasks".
Infering context and generating cohert and contextually relevant responses are what are arguably the most useful to researchers, though even with this as the umbrella skill of LLMs, each will still have customizations (and access to different data that they are able to access and be trained on) that will determine their usefulness to the user. The box below has a few links to some LLM options, navigate to the next page to see more information on some of the more popular LLMs.
The next pages describe select AI tools. Most of these are subscription based with limited free versions. UND provides licensing to Scite. For more tools, see the GenAI Tool Tracking List. The GenAI tool tracking list has tools organized by their best purpose (ex. discovery or image generation). If you would like more assistance on choosing a tool, please contact your librarian.