Events Calendar

PHD Thesis Proposal (Shushan Arakelyan)

Thesis proposal committee members:
Xiang Ren (Chair)
Nenad Medvidovic
Weihang Wang
Jay Kuo
Mukund Raghothaman

Abstract:
Successful deployment of any AI model requires generalization to previously unseen, real-world scenarios. Lack of generalization in models can lead to outcomes ranging from reduced performance to potential legal liabilities. In this thesis, I explore generalization challenges in large language models for code processing. I will discuss three different generalization concerns that language models for code processing can exhibit, and present my progress in building models that can overcome those. 1) I will start by discussing compositional generalization issues, where models must adapt to previously unseen instruction combinations 2) Next I will talk about out-of-domain generalization, and how distribution shifts within single projects or corporations can affect model performance, and how to overcome it. 3) Finally, I will talk about generalization of advanced models to programming languages with fewer resources.

Location: https://usc.zoom.us/j/7195147935?pwd=SU5oS1MrQUxURC9tSFd2Y3VuUDdoQT09

Audiences: Everyone Is Invited

Contact: Asiroh Cham

Event Link: https://usc.zoom.us/j/7195147935?pwd=SU5oS1MrQUxURC9tSFd2Y3VuUDdoQT09