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.

ChatGPT by OpenAI is the most famous and most used auto regressive decoder Transformer based Large Language Model.

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.

I was recently asked a question - how do you find seminal papers/work/research?

As academic librarians helping early-stage researchers (Masters, Phds students), we are often asked to provide guidance on the literature review process in one shot classes.