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.  But here's an extended 40-minute recording before I cut it down. As I was just practicing, the delivery might not be the best, and in fact the final 10-minute version might be better!

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. This seems to be done in two main ways. Firstly, it is used to help generate direct answers to questions.

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.  My suspicion is this feature isn't that well known even among librarians.

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.

ChatGPT by OpenAI is the most famous and most used auto regressive decoder Transformer based Large Language Model. However, it is not the only large language model, besides open ones like BLOOM, Google has developed their own versions include LaMDA, and Pathways Language Model (PaLM) both in 2022, but had never released them any of them, until now.

The story so far. ChatGPT, or other large language models, is not ideal for information retrieval alone because it often hallucinates or fabricates information, including references.

Warning: I am not a data or information scientist; this is a new area I am trying to learn about The rise of large language models, such as GPT-3, GPT-4, ChatGPT, and BERT, has dramatically improved information retrieval in recent years.

Warning: I wrote this to explain to myself high-level technical details in deep learning, it is likely to include inaccuracies.

Warning : Speculative piece! I recently did a keynote at the IATUL 2023 conference where I talked about the possible impact of large language models (LLMs) on academic libraries.

In my last blog post, I tried to identify seminal papers using a variety of methods. These were divided into two main categories. The first category was to look at text written by other authors mentioning that certain works were seminal.  The most straightforward way was to search citation statements/context in scite.ai for keyword phrases like "Seminal works" + Topic.

I was recently asked a question - how do you find seminal papers/work/research? My first thought was just to read (articles, reference works etc)! But my second thought was, that's actually quite an interesting question, as I throw around the term "seminal paper" all the time in workshops.