Earlier related pieces - How Q&A systems based on large language models (eg GPT4) will change things if they become the dominant search paradigm - 9 implications for libraries

Note: This is a lightly edited piece of something I wrote for my institution

A decade ago in 2012, I observed how the dominance of Google had slowly affected how Academic databases and OPACs/ catalogues (now discovery services) work.

On September 2023, OpenAI announced that ChatGPT Plus would be enhanced in three ways

List of academic search engines that use Large Language models for generative answers (for the latest version - see this page)

One of the earliest themes of this blog was to track tools that not only helped with discovery but also with delivery, helping users to gain access to full-text via institution subscriptions (and of course via Open Access) even if they did not start off the library homepage

I've been a subscriber of OpenAI's ChatGPT plus for a while though I have been struggling to justify it to myself for a while until recently.

I was asked to give a talk at CILIP Conference 2023 for the Data & AI panel. I was given 10 minutes to send a recording which I eventually delivered.

As expected more academic search engines are starting to adopt the near-human Natural language understanding and natural language generation capabilities of Large Language Models.

In recent years, Elsevier has provided relatively good access to their content via APIs, whether it be via the Scopus API for metadata including bibliometrics or even full-text via Sciencedirect API as long as you are an institutional customer, typically at no additional fee.

Edit: Since writing this I have watched the Video - State of GPT | BRK216HFS by Andrej Karpathy(Open AI). In 42 minutes, it not only covers the basic high-level details on how auto-regressive decoder only models work (e.g.; GPT models) but has the clearest explanation on why prompt engineering works.