What Are the Common Challenges in Converting Natural Language to SQL Queries?

Below are some common challenges when converting natural language into SQL queries:

• Ambiguity in Requests:
  The intent behind the natural language description might be vague or have multiple interpretations (e.g., “list all sales from last month” might be interpreted differently based on what qualifies as a sale or what “last month” means).

• Mapping to Database Schema:
  Natural language terms may not directly correlate with column names or table structures. In addition, synonyms and colloquial language might need to be mapped to the proper schema identifiers.

• Implicit Relationships and Joins:
  Users may reference related data without explicitly stating join conditions (e.g., “find orders with customer details”). The system must infer which tables to join and on which keys.

• Handling Complex Conditions:
  Some natural language queries include nested conditions or aggregations (like “find the top-selling products in regions that had a growth rate over 5%”). Parsing these conditions into proper subqueries and nested SQL statements can be challenging.

• Ambiguity in Aggregations and Grouping:
  Requests that involve aggregation (e.g., “average sales per region”) need careful interpretation to ensure that the GROUP BY clause and any filtering (like HAVING) are correctly applied.

• Temporal and Comparative Expressions:
  Phrases like “recent”, “last year”, or “greater than average” need additional context to convert them into precise SQL WHERE clauses.

By being aware of these challenges, a system converting natural language to SQL can implement strategies—such as requiring user clarification or utilizing context-aware mappings—to reduce potential errors in translation.