Retrieval-Augmented Strategies for LLM-Based Text-to-SQL Systems: A Systematic Literature Review
Abstract
This OSF project documents the protocol, methodological audit trail, and reproducibility materials for the systematic literature review “Retrieval-Augmented Strategies for LLM-Based Text-to-SQL Systems: A Systematic Literature Review.” The review characterizes how retrieval-augmented strategies are designed and empirically evaluated in large language model (LLM)-based Text-to-SQL systems. It focuses on approaches that dynamically retrieve contextual information—such as database schemas, values, natural language–SQL examples, documentation, domain knowledge, or other structured knowledge—and use the retrieved information to support SQL generation, reasoning, decomposition, refinement, validation, or correction. The review covers peer-reviewed journal articles published in English within a search window from January 2022 to August 19, 2026. Systematic searches were conducted in Scopus, Web of Science Core Collection, and IEEE Xplore. Eligible studies were required to investigate natural-language-to-SQL generation, directly involve an LLM in the Text-to-SQL pipeline, incorporate a retrieval-augmented mechanism according to the predefined operational definition, and report an empirical evaluation. The review addresses four research dimensions: (1) the retrieval strategies and types of contextual information used; (2) the LLMs, datasets, database domains, and experimental configurations employed; (3) the evaluation metrics, baselines, and reported effects of retrieval on…
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