Eric - it sounds like we may be at about the same point: I am wanting to start working in the area of fine-tuning, specifically focusing on Chat-GPT generated data management plans that would then be revised by experts and used as a fine-tuning data corpus for (hopefully) improving the draft DMP language provided by Chat-GPT. This is part of a broader experimentation with DMP generation prompts derived from machine-readable DMP content.
Thanks, Karl Karl Benedict Director of Research Data Services/ Director of IT College of University Libraries and Learning Sciences University of New Mexico Office: Centennial Science and Engineering Library, Room L173 Make an Appointment: https://outlook.office365.com/owa/calendar/karlbened...@unmm.onmicrosoft.com/bookings/ On 26 Feb 2024, at 14:05, Eric Lease Morgan wrote: > [You don't often get email from > 00000107b9c961ae-dmarc-requ...@lists.clir.org. Learn why this is important at > https://aka.ms/LearnAboutSenderIdentification ] > > [EXTERNAL] > > Who out here in Code4Lib Land is practicing with either one or both of the > following things: 1) fine-tuning large-language models, or 2) > retrieval-augmented generation (RAG). If there is somebody out there, then > I'd love to chat. > > When it comes to generative AI -- things like ChatGPT -- one of the first > things us librarians say is, "I don't know how I can trust those results > because I don't know from whence the content originated." Thus, if we were > create our own model, then we can trust the results. Right? Well, almost. The > things of ChatGPT are "large language models" and the creation of such things > are very expensive. They require more content than we have, more computing > horsepower than we are willing to buy, and more computing expertise than we > are willing to hire. On the other hand there is a process called > "fine-tuning", where one's own content is used to supplement an existing > large-language model, and in the end the model knows about one's own content. > I plan to experiment with this process; I plan to fine-tune an existing > large-language model and experiment with it use. > > Another approach to generative AI is called RAG -- retrieval-augmented > generation. In this scenerio, one's content is first indexed using any number > of different techniques. Next, given a query, the index is searched for > matching documents. Third, the matching documents are given as input to the > large-language model, and the model uses the documents to structure the > result -- a simple sentence, a paragraph, a few paragraphs, an outline, or > some sort of structured data (CSV, JSON, etc.). In any case, only the content > given to the model is used for analysis, and the model's primary purpose is > to structure the result. Compared to fine-tuning, RAG is computationally dirt > cheap. Like fine-tuning, I plan to experiment with RAG. > > To the best of my recollection, I have not seen very much discussion on this > list about the technological aspects of fine-tuning nor RAG. If you are > working these technologies, then I'd love to hear from you. Let's share war > stories. > > -- > Eric Morgan <emor...@nd.edu> > Navari Family Center for Digital Scholarship > University of Notre Dame
