Converting Natural Language to Query Languages Using Large Language Models: A Systematic Literature Review - Lima, Rayfran Rocha; Vasconcelos, Kamila Cardoso; Gadelha, Eloisa Mendonça (Proceedings of the Brazilian Symposium on Multimedia and the Web (WebMedia); 2025: Proceedings of the 31st Brazilian Symposium on Multimedia and the WebDO - 10.5753/webmedia.2025.15123, 2025)
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.