LegacyX Large Language Model – Knowledge retention, sharing and mobility

In this project, we will develop a system to revolutionize how knowledge is captured, retained, shared, managed and utilized, particularly in software development and user support. Our aim is to capture essential knowledge that has accumulated by experts and is often overlooked, organizing it systematically for easy access and application by others. A key component of our approach is the advanced fine-tuning of large language models (LLMs) using cutting-edge machine learning techniques. This will allow us to tailor these models specifically to our needs, ensuring the knowledge is not just stored but also effectively processed and made readily available.
For LegacyX, this project represents a significant advancement in operational efficiency and customer service. The techniques and insights gained from this project will also benefit other sectors, demonstrating the broader impact and potential of our research, offering substantial benefits to both LegacyX and the wider Canadian community.

Faculty Supervisor:

Irene Cheng

Student:

Partner:

UnionX Software

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

University of Alberta

Program: