Natural language to SQL (NL-to-SQL) systems are increasingly critical in industry for enabling non-technical users to access structured data efficiently, supporting faster decision-making and data accessibility. However, state-of-the-art systems often depend on large proprietary models, which introduce serious concerns around privacy. While open-source LLMs offer a viable substitute, high-performing variants (e.g., 70B or 405B) require substantial GPU memory, making them impractical for many production environments. Smaller open-source models that fit on a single 80GB GPU present a more deployable alternative, yet existing efforts to enhance their Text-to-SQL performance rely heavily on fine-tuning, limiting flexibility. We propose RoSL, a plug-and-play framework that improves SQL generation for smaller LLMs without any task-specific training. While schema linking is often omitted for larger models, we show it remains essential for smaller ones. Further, we are the first to apply question decomposition at the schema linking stage, rather than during SQL generation as in prior work, to address the precision-recall tradeoff. Our approach improves schema linking recall by 25.1% and execution accuracy by 8.2% on the BIRD benchmark using ibm-granite/granite-3.3-8b-instruct, making it an effective and industry-friendly NL-to-SQL solution. We further analyze RoSL’s latency–efficiency characteristics, showing that it maintains practical efficiency for real-world deployment.

In this paper, we propose a method to improve the reasoning capabilities of Visual Question Answering (VQA) systems by integrating Dense Passage Retrievers (DPRs) with Vision Language Models (VLMs). While recent works focus on the application of knowledge graphs and chain-of-thought reasoning, we recognize that the complexity of graph neural networks and end-to-end training remain significant challenges. To address these issues, we introduce **R**elevance **G**uided **VQA** (**RG-VQA**), a retriever-generator pipeline that uses DPRs to efficiently extract relevant information from structured knowledge bases. Our approach ensures scalability to large graphs without significant computational overhead. Experiments on the ScienceQA dataset show that RG-VQA achieves state-of-the-art performance, surpassing human accuracy and outperforming GPT-4 by more than . This demonstrates the effectiveness of RG-VQA in boosting the reasoning capabilities of VQA systems and its potential for practical applications.