Towards Efficient and Domain-aware Adaptation of Large Foundation Models

PhD Thesis Proposal Defence


Title: "Towards Efficient and Domain-aware Adaptation of Large Foundation
Models"

by

Mr. Shizhe DIAO


Abstract:

In the burgeoning realm of artificial intelligence, foundation models stand out
as a pivotal advancement. Demonstrating an unmatched ability in human-like text
comprehension and generation, these models have set new standards in a wide
array of natural language processing applications. Their general-purpose nature
allows impressive performance across diverse tasks, yet domain-aware adaptation
can further amplify their efficacy. The challenge is to fine-tune these models
to specific domains efficiently and effectively. In this thesis, we offer an
in-depth exploration into taming language and vision-language models to
particular domains. Our focus is on architectural modifications, training
strategies, and prompting methods that enhance large foundation model
performance in specific domains while ensuring the adaptation is
resourceefficient, scalable, and effective.


Date:                   Thursday, 26 October 2023

Time:                   2:00pm - 4:00pm

Venue:                  Room 5562
                        lifts 27/28

Committee Members:      Prof. Tong Zhang (Supervisor)
                        Prof. Raymond Wong (Chairperson)
                        Dr. Junxian He
                        Dr. Yangqiu Song


**** ALL are Welcome ****