Context
Large language models (LLMs) have became popular over the last few years, one of the reason being the quality of the outputs these models generate. LLM Agents are a step further, they allow LLMs to use memory, tools and sequential thinking.
Motivation
- Can we utilize LLM Agents for reasoning about code, to repair it for instance?
- Does additional feedback from tools that agents can use improve the quality of repairs?
Goal
The student will follow 3 steps:
- Review about Code Agents
- Practical direction: Code Repair, Vulnerability Repair, Code Generation
- Experiments and analysis of results where the second step is to be discussed further
Requirements
- Knowledge of Machine Learning, PyTorch or TensorFlow
- Passion for learning about state-of-the-art methods and models