Track 2: LLM Adaptation for Coding Tasks
Track leader at TU Delft: Maliheh Izadi (Assistant Professor)Track leader at JetBrains: Egor Bogomolov (Code Modeling Research team lead)
Given the competitive landscape surrounding the use of AI today, mere development and deployment of LLM in the IDE does not suffice. On one hand, the current approach of shipping/querying the same generic model for every task, project, and user will not provide optimal results. On the other hand, researchers have continuously trained ever-larger models which require large amounts of training data. This data is usually a massive unsanitized corpus extracted from public domains. Research has shown the resulting LLMs can memorize their training data and emit verbatim [1] leading to legal issues. However, the models are less proficient outside their training data and may struggle when performing tasks in previously unencountered repositories. As new generations of models are being rolled out, there is a need to assess the emerging capabilities of such models.
This project proposes to adapt, personalize, and evaluate the giant generic language models to different scenarios to yield tangible, timely, safe, and personalized outputs for the end-users.
PhD Students:
- Egor Bogomolov (JetBrains)
- Danielle Cipollone (TU Delft)
MSc Students:
- Tim van Dam (graduated in 2024): Thesis
Track news
Publications
Projects
- 8th of July 2024: Enriching Source Code with Contextual Data for Code Completion Models
