The field of text-to-query language is experiencing significant growth, with a focus on developing more efficient and accurate systems. Researchers are exploring the use of large language models, retrieval-augmented generation, and graph databases to improve the performance of text-to-SQL and text-to-Cypher systems. The integration of error correction mechanisms, embedding fine-tuning, and external knowledge bases is also being investigated to enhance the accuracy and transparency of these systems. Noteworthy papers include GEMMA-SQL, which achieves state-of-the-art performance on the SPIDER benchmark, and Multi-Agent GraphRAG, which proposes a modular LLM agentic system for text-to-Cypher query generation. Additionally, the development of lightweight, ontology-agnostic parsers such as S2CLite is enabling the translation of SPARQL queries into Cypher queries with high accuracy.