The field of text-to-SQL parsing is witnessing significant advancements, driven by the development of large language models (LLMs) and innovative techniques for improving their reliability and accuracy. Researchers are focusing on providing calibrated confidence scores for LLM-based text-to-SQL systems, leveraging the structured nature of SQL queries to improve error detection and correction. Another notable trend is the exploration of resource-efficient architectures for text-to-SQL translation, enabling the use of powerful LLMs in constrained environments. Furthermore, the creation of large-scale repositories of word associations is facilitating research on social norms and stereotypes. Noteworthy papers include Calibrating LLMs for Text-to-SQL Parsing by Leveraging Sub-clause Frequencies, which proposes a method for text-to-SQL calibration using sub-clause frequency scores. SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL is another notable work, presenting an end-to-end framework for fine-grained detection and correction of semantic errors in LLM-generated SQL.