Track 2: LLM Adaptation
Track leader at TU Delft: Maliheh IzadiTrack leader at JetBrains: Egor Bogomolov
This track aims to refine generic large language models for code to suit various scenarios. By tailoring these models to the specific needs of individual users, projects, and organizations, we can ensure personalized outputs. The models will be optimized to produce legal, safe, and timely predictions and operate efficiently on low-resource devices.
Track news
15 July 2024:
MSc students graduated
14 July 2024:
ACM Distinguished Paper Award at AIWare 2024
22 April 2024:
Strong AI4SE presence at ICSE 2024
Projects
- 8th of July 2024: Enriching Source Code with Contextual Data for Code Completion Models