Text-to-SQL: Natural Language Database Querying with Large Language Models

Status: public · Confidence: medium (0.8) · Basis: verified_sources

## TL;DR
Text-to-SQL converts natural-language questions into database queries. Strong public claims should stay tied to datasets, semantic parsing methods, and decoding constraints.

## Core Explanation
A text-to-SQL system must understand a user's question, map it to a database schema, and produce executable SQL. The challenge is harder when schemas are unseen, table relationships are complex, or the user's wording is ambiguous.

## Detailed Analysis
The repaired evidence focuses on three stable anchors: early neural SQL generation, the Spider cross-domain benchmark, and constrained decoding through PICARD. It avoids unsupported claims about production accuracy or autonomous database access.

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