This research introduces Plan-of-SQLs (POS), a new approach that makes Large Language Models (LLMs) more transparent when answering questions about tabular data.

• Enables users to understand model reasoning by providing SQL-based explanations
• Designed for high-stakes industries where decision transparency is critical
• Improves trust and verification in model-generated answers
• Particularly valuable for healthcare applications where interpretable AI decisions directly impact patient care and medical data analysis

For medical professionals, this research offers a critical advancement toward more trustworthy AI systems that can explain their table-based reasoning process, essential for clinical decision support and healthcare data interpretation.

Original Paper: Interpretable LLM-based Table Question Answering