  Cross-domain and cross-compositional generalization of Text-to-SQL semantic
parsing is a challenging task. Existing Large Language Model (LLM) based
solutions rely on inference-time retrieval of few-shot exemplars from the
training set to synthesize a run-time prompt for each Natural Language (NL)
test query. In contrast, we devise an algorithm which performs offline sampling
of a minimal set-of few-shots from the training data, with complete coverage of
SQL clauses, operators and functions, and maximal domain coverage within the
allowed token length. This allows for synthesis of a fixed Generic Prompt (GP),
with a diverse set-of exemplars common across NL test queries, avoiding
expensive test time exemplar retrieval. We further auto-adapt the GP to the
target database domain (DA-GP), to better handle cross-domain generalization;
followed by a decomposed Least-To-Most-Prompting (LTMP-DA-GP) to handle
cross-compositional generalization. The synthesis of LTMP-DA-GP is an offline
task, to be performed one-time per new database with minimal human
intervention. Our approach demonstrates superior performance on the KaggleDBQA
dataset, designed to evaluate generalizability for the Text-to-SQL task. We
further showcase consistent performance improvement of LTMP-DA-GP over GP,
across LLMs and databases of KaggleDBQA, highlighting the efficacy and model
agnostic benefits of our prompt based adapt and decompose approach.
