arXiv:2606.10125v1 Announce Type: cross Abstract: Few-shot example retrieval is the dominant paradigm for grounding large language models (LLMs) in domain-specific text-to-SQL systems. However, the quality of the annotated example bank directly governs system accuracy, and expert annotation is prohibitively expensive. We formalize the active selection of these examples as a constrained experimental design problem over the intrinsic, low-dimensional manifold of semantic query embeddings. Unlike standard active learning frameworks, our setting introduces three critical challenges: varying, query
The proliferation of LLMs and their application in domain-specific tasks like Text-to-SQL is driving urgent research into optimizing their performance and reducing high operational costs.
Improving few-shot example selection in Text-to-SQL systems will significantly enhance the accuracy and reduce the annotation expenses of domain-specific LLM deployments, accelerating their practical adoption.
The efficiency and cost-effectiveness of custom LLM applications, particularly in enterprise data environments, can be substantially improved through more robust active learning techniques.
- · AI developers
- · Enterprise software companies
- · Data-intensive industries
- · LLM-based service providers
- · Manual data annotators
- · Companies relying on expensive custom LLM fine-tuning
- · Inefficient AI deployment strategies
More accurate and cost-effective domain-specific LLM implementations.
Accelerated adoption of AI agents and automated data querying across various industries, enhancing data-driven decision-making.
Increased demand for advanced active learning and data efficiency techniques, shifting R&D focus within AI.
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Read at arXiv cs.LG