Converting Natural Language to Query Languages Using Large Language Models: A Systematic Literature Review
Resumo
The task of converting natural language commands into query languages (NL-to-QL) has gained attention due to its potential to improve data accessibility for non-technical users. Although several studies have explored rule-based and sequence-to-sequence models, there is still a lack of a literature review that presents the impact of using large language models (LLMs) on this task. As an output of a systematic literature review, this paper examines how recent studies have utilized LLMs by applying fine-tuning or prompt engineering techniques to address this task. Presenting a compilation of methods, architectures, and techniques, as well as evaluation metrics, datasets, and benchmarks applied, including the available competitions and educational platforms, it provides a comprehensive overview of NL-to-QL conversion, mapping current advancements, future research directions, and remaining challenges, including issues with schema generalization, query interpretability, and hallucination mitigation.
Palavras-chave:
Txt2SQL, NL-to-QL, NL Query to Query Language
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Publicado
10/11/2025
Como Citar
LIMA, Rayfran Rocha; VASCONCELOS, Kamila Cardoso; GADELHA, Eloisa Mendonça.
Converting Natural Language to Query Languages Using Large Language Models: A Systematic Literature Review. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 31. , 2025, Rio de Janeiro/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 598-608.
DOI: https://doi.org/10.5753/webmedia.2025.15123.
