  Large Language Models (LLMs) have spurred progress in text-to-SQL, the task
of generating SQL queries from natural language questions based on a given
database schema. Despite the declarative nature of SQL, it continues to be a
complex programming language. In this paper, we investigate the potential of an
alternative query language with simpler syntax and modular specification of
complex queries. The purpose is to create a query language that can be learned
more easily by modern neural semantic parsing architectures while also enabling
non-programmers to better assess the validity of the query plans produced by an
interactive query plan assistant.
  The proposed alternative query language is called Query Plan Language (QPL).
It is designed to be modular and can be translated into a restricted form of
SQL Common Table Expressions (CTEs). The aim of QPL is to make complex data
retrieval accessible to non-programmers by allowing users to express their
questions in natural language while also providing an easier-to-verify target
language. The paper demonstrates how neural LLMs can benefit from QPL's
modularity to generate complex query plans in a compositional manner. This
involves a question decomposition strategy and a planning stage.
  We conduct experiments on a version of the Spider text-to-SQL dataset that
has been converted to QPL. The hierarchical structure of QPL programs enables
us to measure query complexity naturally. Based on this assessment, we identify
the low accuracy of existing text-to-SQL systems on complex compositional
queries. We present ways to address the challenge of complex queries in an
iterative, user-controlled manner, using fine-tuned LLMs and a variety of
prompting strategies in a compositional manner.
