BISCUIT: Scaffolding LLM-Generated Code with Ephemeral UIs in Computational Notebooks
arxiv(2024)
摘要
Novices frequently engage with machine learning tutorials in computational
notebooks and have been adopting code generation technologies based on large
language models (LLMs). However, they encounter difficulties in understanding
and working with code produced by LLMs. To mitigate these challenges, we
introduce a novel workflow into computational notebooks that augments LLM-based
code generation with an additional ephemeral UI step, offering users UI-based
scaffolds as an intermediate stage between user prompts and code generation. We
present this workflow in BISCUIT, an extension for JupyterLab that provides
users with ephemeral UIs generated by LLMs based on the context of their code
and intentions, scaffolding users to understand, guide, and explore with
LLM-generated code. Through 10 user studies where novices used BISCUIT for
machine learning tutorials, we discover that BISCUIT offers user semantic
representation of code to aid their understanding, reduces the complexity of
prompt engineering, and creates a playground for users to explore different
variables and iterate on their ideas. We discuss the implications of our
findings for UI-centric interactive paradigm in code generation LLMs.
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