Large Language Models for Research
Description
This workshop will take place on Wednesday, 8th April 9:30-12:30pm AEST.
Large Language Models (LLMs), such as Llama, GPT, and Mistral, are transforming how researchers discover, manage, and analyse information. In this interactive 3-hour workshop, participants will be introduced to practical, reproducible workflows using LLMs for literature review, data preparation, coding, analysis, and research writing. By interacting with LLMs via Python and Jupyter Notebooks, participants will learn how to automate summarisation and organisation of scientific literature using an LLM. They will build a simple Retrieval-Augmented Generation (RAG) workflow to answer research questions. Understand how to generate and debug code with natural language prompts and discover how an LLM can be used to clean up messy text data and structure results for analysis.
FAQs
What should I bring into the event?
This is an online workshop, so you will need to provide your own computer, and have access to a reliable internet connection sufficient for video conferencing. A second monitor is highly recommended. This workshop will run on the Google Colab platform. Setup instructions will be provided prior to the workshop.
What prior experience do I need?
Basic Python knowledge for example having completed QCIF’s ‘Plotting and Programming in Python’ or an introductory Python programming course or workshop.
Who should attend?
This course is ideal for researchers across disciplines looking to integrate LLMs into their research workflow with minimal technical barriers.
How can I contact the organiser with any questions?
You can email training@qcif.edu.au.
What’s the cancellation policy?
Cancellations may be made up to 3 working days prior to the start of the workshop. If you do not join on the day without having cancelled beforehand, you may be blocked from attending any future QCIF training workshops.
Online Event
Online workshop delivered via Zoom. Full setup and connection details will be sent by email two working days before the course.
Refund policy
Refunds are available up to 3 working days prior to the event.
