  Despite the increase in popularity of language models for code generation, it
is still unknown how training on bimodal coding forums affects a model's code
generation performance and reliability. We, therefore, collect a dataset of
over 2.2M StackOverflow questions with answers for finetuning. These fine-tuned
models have average $pass@k$ improvements of 54.64% and 85.35% on the HumanEval
(Chen et al., 2021) and Mostly Basic Program Problems (Austin et al., 2021)
tasks, respectively. This regime further decreases the number of generated
programs with both syntax and runtime errors. However, we find that at higher
temperatures, there are significant decreases to the model's ability to
generate runnable programs despite higher $pass@k$ scores, underscoring the
need for better methods of incorporating such data that mitigate these side
effects. The code can be found
https://github.com/gabeorlanski/bimodalcode-generation
