The field of Natural Language to SQL (NL2SQL) generation is moving towards more accurate and robust models, with a focus on addressing the challenges of semantic gaps and poor benchmark quality. Recent work has introduced novel frameworks and datasets to improve the performance of NL2SQL models, including the use of guided generation, SQL2Text back-translation validation, and task decomposition. These advancements have led to significant improvements in execution accuracy and have highlighted the need for more rigorous dataset curation. Noteworthy papers include: GBV-SQL, which proposes a multi-agent framework for semantic validation and achieves a 5.8% absolute improvement on the BIRD benchmark. DeKeyNLU, which presents a novel dataset for refining task decomposition and enhancing keyword extraction precision, and achieves significant improvements in SQL generation accuracy on the BIRD and Spider dev datasets.