New Algorithm Reduces Annotation Costs for Few-Shot Text-to-SQL Systems
Researchers have developed a stratified greedy algorithm that selectively chooses which examples to annotate for training text-to-SQL systems, addressing the challenge of expensive expert annotation. The method handles three key complications: varying annotation reliability, the need for semantic diversity, and uncertainty about the underlying data structure. The approach maintains theoretical guarantees and empirically reduces labeling effort while preserving system accuracy.
A new active learning approach tackles the problem of efficiently selecting which examples to annotate for few-shot text-to-SQL systems powered by large language models. The researchers formalize example selection as a constrained experimental design problem, accounting for three realistic challenges: heteroscedastic annotation reliability (where annotation quality varies by query), the need for spatial diversity across semantic topics, and unknown covariance structure in the embedding space. They propose a stratified greedy algorithm that maximizes a heteroscedastic mutual information objective, proving it remains submodular with a theoretical constant-factor approximation guarantee. The method includes a spectral bound showing graceful degradation when assumptions about the underlying data-generating process are violated. Empirical validation demonstrates the strategy significantly reduces labeling effort while maintaining high text-to-SQL retrieval accuracy.
What's missing
The paper does not specify which text-to-SQL benchmarks or datasets were used for empirical evaluation, the magnitude of labeling cost reduction achieved, or how the method compares to other active learning baselines in quantitative terms.
What different sources said
- arXiv cs.LGCenter
Robust Active Learning for Few-Shot Example Selection in Text-to-SQL
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