Papers › Evaluating How Fine-tuning on Bimodal Data Effects Code Generation
Evaluating How Fine-tuning on Bimodal Data Effects Code Generation
Gabriel Orlanski, Seonhye Yang, Michael Healy
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
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