Hugo Larochelle

On-the-Fly Adaptation of Source Code Models using Meta-Learning

2020 · arXiv (Cornell University)

The ability to adapt to unseen, local contexts is an important challenge that successful models of source code must overcome. One of the most popular approaches for the adaptation of such models is dynamic evaluation. …

Repository-Level Prompt Generation for Large Language Models of Code

2022 · arXiv (Cornell University)

With the success of large language models (LLMs) of code and their use as code assistants (e.g. Codex used in GitHub Copilot), techniques for introducing domain-specific knowledge in the prompt design process become important. In …

Language GANs Falling Short

2020 · International Conference on Learning Representations

Traditional natural language generation (NLG) models are trained using maximum likelihood estimation (MLE) which differs from the sample generation inference procedure. During training the ground truth tokens are passed to the model, however, during inference, …