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Aaron Tay

I was asked to give a talk at CILIP Conference 2023 for the Data &amp; 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!

Recorded extended talk - The possible impact of AI on search and discovery​

In the last blog post , I argued that despite the advancements in AI thanks to transformer based large language models, most academic search still are focused mostly in supporting exploratory searches and do not focus on optimizing recall and in fact trade off low latency for accuracy.

Undermind.ai - a different type of AI agent style search optimized for high recall?

I've watched with interest, as academic search engines use AI to improve searching.  Elicit is probably currently the leading example of this, using transformer based language models to

A conceptual view of information retrieval - can we do better with AI?

One of the tricks about using the newer "AI powered" search systems like Elicit, SciSpace and even JSTOR experiment search is that they recommend that you type in your query or what you want in full natural language and not keyword search style (where you drop the stop words) for better results. So for example do

Why entering your query in natural question leads to better result than keyword searching with the latest AI powered (Dense retrieval/embedding models) search

I've spent a large part of my career as an academic librarian studying the question of discovery from many angles.

How learning evidence synthesis (systematic reviews etc) changed the way I search and some thoughts about semantic search as a complement search technique

Note: This is a lightly edited piece of something I wrote for my institution What is Google’s Search Generative Experience (SGE)?   In past ResearchRadar pieces, we have discussed about how search engines both general (e.g. Bing Chat, Perplexity) and academic (e.g Elicit, Scite Assistant, Scopus (upcoming)) are integrating search with generative AI (via Large Language Models) using techniques like RAG (Retrieval Augmented Generation).  But what

Google’s Search Generative Experience (SGE) is now available in 120+ countres. What you need to know

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.

JSTOR generative AI pilot - Or is Semantic Search coming for academic databases?

On September 2023, OpenAI announced that ChatGPT Plus would be enhanced in three ways  1. It would allow you to speak directly with GPT and it would also be able to reply in voice  2. It would be able to create images using DALL-E 3, OpenAI's image generation model  3. It would be able to accept image inputs  Since I finally gained access to these features, I will briefly review them with my thoughts on how impactful they might be for

ChatGPT Plus - new DALL-E 3 (image creation) &amp;  Vision (image recognition) capability - A quick overview &amp; why I am disappointed.

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


This is a non-comprehensive list of academic search engines that use generative AI (almost always Large language models) to generate direct answers on top of list of relevant results, typically using Retrieval Augmented Generation (RAG) Techniques. We expect a lot more!

List of academic search engines that use Large Language models for generative answers and some factors to consider when using

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.

Two new browser extensions that help with delivery or access - ebsco passport and GetFTR browser extension and other developments

As academic search engines and databases incorporate the use of generative AI into their systems, an important concept that all librarian should grasp is that of retrieval augmented generation (RAG).   You see it in use in all sorts of "AI products" today from chatbots like Bing Copilot, to Adobe's Acrobat Ai assistant that allow you to chat with your PDF.

Retrieval Augmented Generation and academic search engines - some suggestions for system builders

Let's be clear here,

 Google Scholar is ill designed for use for systematic reviews

. I am not trying to argue otherwise. (Obligatory warning, I am not a real systematic review librarian) But why exactly?

Why EXACTLY is Google Scholar bad for evidence synthesis, systematic reviews?

Warning : I am not a information retrieval researcher, so take my blog post with a pinch of salt

In my last blog post, I described a simplified description of a framework for infomation retrieval from the paper -

Deep dive into AI powered embedding based search - sparse vs dense embeddings

Can Semantic Search be more interpretable? COLBERT, SPLADE might be the answer but is it enough?

I have been writing about what I call citation based literature mapping tools for over 5 years now, starting from Citation Gecko in 2018.

All about citation chasing and tools that does citation chasing like Citation Gecko, Connected papers, Research Rabbit, LitMaps and more

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

In the ever-evolving landscape of information retrieval and library science, the emergence of large language models, particularly those based on the transformer architecture like GPT-4, has opened up a Pandora's box of possibilities and challenges.

Things I am still wondering about generative AI + Search in 2024 - impact of semantic search, generation of answers with citations and more..

I recently watched a librarian give a talk about their experiments teaching prompt engineering. The librarian drawing from the academic literature on the subject (there are lots!), tried to leverage "prompt engineering principles" from one such paper to craft a prompt and used it in a Retrieval Augmented Generation (RAG) system, more specifically, Statista's brand new "research AI" feature.

Prompt engineering with Retrieval Augmented Generation systems - tread with caution!

IP and ethical issues surrounding the use of content in Large Language Models (LLMs) have sparked significant debate, but I’ve mostly stayed out of it as this isn’t my area of expertise, and while there’s much to discuss and many legal opinions to consider, ultimately, the courts will decide what’s legal. However, for those interested in exploring this topic further, I recommend Peter Schoppert’s AI &amp; Copyright substack.

AI &amp; Retrieval augmented generation search - the content problem -reactions from librarians, authors and publishers &amp; thoughts on tradeoffs

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.

Aaron Tay's Musings about librarianship

All about citation chasing and tools that does citation chasing like Citation Gecko, Connected papers, Research Rabbit, LitMaps and more