Kyunghyun Cho

We introduce Imitation Learning from Language Feedback (ILF), demonstrate that large language models accurately incorporate natural language feedback and that finetuning with ILF scales well with the dataset size, even outperforming finetuning on human summaries.

We introduce Imitation Learning from Language Feedback (ILF) to improve code generation, demonstrating that a small amount of natural language feedback during training can lead to significant performance gains on program synthesis benchmarks.

We propose a three-step learning algorithm to learn from natural language feedback, which conveys more information per human evaluation than comparisons.