<ul><li>Evaluation is crucial for NLQ-to-SQL systems to ensure the generated SQL queries reflect user intent and retrieve accurate data.</li><li>Measurable metrics are essential to assess NLQ-to-SQL system performance beyond human validation.</li><li>An NLQ-to-SQL pipeline powered by LLMs aims to bridge the gap between natural language and structured data.</li><li>Metrics like Entity Recognition Score help evaluate correctness and efficiency of generated SQL queries.</li><li>Semantic Equivalence Score ensures functionally correct queries are generated by NLQ-to-SQL models.</li><li>Halstead Complexity Score measures the complexity of generated SQL queries to improve model performance.</li><li>SQL Injection Detection is vital for identifying and preventing malicious patterns in queries.</li><li>Data Retrieval Accuracy assesses how well generated SQL queries retrieve data compared to ground truth queries.</li><li>Monitoring resource utilization during SQL execution helps optimize query performance and database efficiency.</li><li>Evaluation and governance play a crucial role in developing a meaningful NLQ-to-SQL system aligned with user intent.</li></ul>

NLQ-to-SQL Evaluation: The Metrics That Matter

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