I took note of something recently from the Library Innovation Lab at Harvard Law School: WARC-GPT: An Open-Source Tool for Exploring Web Archives Using AI. It takes the contents of WARC files and feeds them into a Retrieval Augmented Generation tool. Been meaning to play with it as a way to enhance FOLIO's docmuentation search, but haven gotten around to it.
Not a war story, perhaps, but a WARC story. ;-) Peter On Feb 26, 2024 at 4:07 PM -0500, Eric Lease Morgan <00000107b9c961ae-dmarc-requ...@lists.clir.org>, wrote: > 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
