New Algorithm Reduces Annotation Costs for Few-Shot Text-to-SQL Systems
Researchers have proposed a stratified greedy algorithm that uses active learning to more efficiently select few-shot examples for text-to-SQL large language model systems. The method addresses three core challenges in this setting: variable annotation reliability, the need for spatial diversity in examples, and uncertainty about the underlying data structure. The approach could significantly lower the cost of expert annotation while preserving system accuracy.
A preprint posted to arXiv introduces a framework for actively selecting high-quality annotated examples used to ground large language models (LLMs) in text-to-SQL tasks, where natural language queries are translated into database queries. The authors formalize this selection process as a constrained experimental design problem over the low-dimensional manifold of semantic query embeddings. Their proposed stratified greedy algorithm maximizes a heteroscedastic mutual information objective, accounting for the fact that annotation quality varies depending on the query. The researchers prove the objective is submodular and approximately monotonic, providing a theoretical constant-factor approximation guarantee, and further show this guarantee degrades gracefully rather than catastrophically when the assumed data model diverges from reality. Empirical results reported in the paper indicate the method meaningfully reduces labeling effort without sacrificing retrieval accuracy in text-to-SQL pipelines.
What's missing
As a preprint, the work has not yet undergone peer review. The generalizability of the approach to domains outside text-to-SQL remains an open question.
What different sources said
- arXiv cs.LGCenter
Robust Active Learning for Few-Shot Example Selection in Text-to-SQL
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