  Large language models (LLMs) have exhibited a strong promise in automatically
generating executable code from natural language descriptions, particularly
with interactive features that allow users to engage in the code-generation
process by instructing the LLM with iterative feedback. However, existing
interaction paradigms often assume that users have expert knowledge to debug
source code and are not optimized for non-professional programmers' use. This
raises challenges in making interactive code generation more accessible for
individuals with varying levels of programming expertise. To tackle these
challenges, we present IntelliExplain, which offers a novel human-LLM
interaction paradigm to enhance non-professional programmers' experience by
enabling them to interact with source code via natural language explanations.
Users interact with IntelliExplain by providing natural language corrective
feedback on errors they identify from the explanations. Feedback is used by the
system to revise the code, until the user is satisfied with explanations by the
system of the code. Our user study demonstrates that users with IntelliExplain
achieve a significantly higher success rate 11.6% and 25.3% better than with
vanilla GPT-3.5, while also requiring 39.0% and 15.6% less time in Text-to-SQL
and Python code generation tasks, respectively.
