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Large Language Models (LLMs) have spurred progress in text-to-SQL, the taskof generating SQL queries from natural language questions based on a givendatabase schema. Despite the declarative nature of SQL, it continues to be acomplex programming language. In this paper, we investigate the potential of analternative query language with simpler syntax and modular specification ofcomplex queries. The purpose is to create a query language that can be learnedmore easily by modern neural semantic parsing architectures while also enablingnon-programmers to better assess the validity of the query plans produced by aninteractive 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 ofSQL Common Table Expressions (CTEs). The aim of QPL is to make complex dataretrieval accessible to non-programmers by allowing users to express theirquestions in natural language while also providing an easier-to-verify targetlanguage. The paper demonstrates how neural LLMs can benefit from QPL'smodularity to generate complex query plans in a compositional manner. Thisinvolves a question decomposition strategy and a planning stage.  We conduct experiments on a version of the Spider text-to-SQL dataset thathas been converted to QPL. The hierarchical structure of QPL programs enablesus to measure query complexity naturally. Based on this assessment, we identifythe low accuracy of existing text-to-SQL systems on complex compositionalqueries. We present ways to address the challenge of complex queries in aniterative, user-controlled manner, using fine-tuned LLMs and a variety ofprompting strategies in a compositional manner.

Semantic Parsing for Complex Data Retrieval: Targeting Query Plans vs. SQL for No-Code Access to Relational Databases