  Code-generating large language models translate natural language into code.
However, only a small portion of the infinite space of naturalistic utterances
is effective at guiding code generation. For non-expert end-user programmers,
learning this is the challenge of abstraction matching. We examine this
challenge in the specific context of data analysis in spreadsheets, in a system
that maps the users natural language query to Python code using the Codex
generator, executes the code, and shows the result. We propose grounded
abstraction matching, which bridges the abstraction gap by translating the code
back into a systematic and predictable naturalistic utterance. In a
between-subjects, think-aloud study (n=24), we compare grounded abstraction
matching to an ungrounded alternative based on previously established query
framing principles. We find that the grounded approach improves end-users'
understanding of the scope and capabilities of the code-generating model, and
the kind of language needed to use it effectively.
