The field of text-to-SQL and natural language interfaces for databases is rapidly evolving, with a focus on improving the accuracy and usability of these systems. Recent developments have centered around the use of large language models (LLMs) to generate SQL queries from natural language questions, as well as the creation of more comprehensive benchmarks to evaluate the performance of these systems. Additionally, there is a growing emphasis on incorporating human feedback and domain knowledge into these systems to improve their accuracy and adaptability. Noteworthy papers in this area include: Continual Learning of Domain Knowledge from Human Feedback in Text-to-SQL, which introduces a framework for continual learning from human feedback in text-to-SQL. FLOWER: Flow-Oriented Entity-Relationship Tool, which presents a flow-oriented entity-relationship tool that automatically detects built-in constraints and creates correct and necessary ones using dynamic sampling and robust data analysis techniques. Beyond SELECT: A Comprehensive Taxonomy-Guided Benchmark for Real-World Text-to-SQL Translation, which proposes a novel taxonomy for text-to-SQL classification and introduces a taxonomy-guided dataset synthesis pipeline. SQL-to-Text Generation with Weighted-AST Few-Shot Prompting, which proposes an architecture that integrates structural query representations and LLM prompting to generate natural language descriptions from SQL queries. Node-Level Uncertainty Estimation in LLM-Generated SQL, which presents a practical framework for detecting errors in LLM-generated SQL by estimating uncertainty at the level of individual nodes in the query's abstract syntax tree. Natural Language Interfaces for Databases: What Do Users Think?, which investigates the usability challenges of natural language interfaces for databases and presents a mixed-method user study comparing a state-of-the-art NL2SQL system with a traditional SQL analytics platform. Castle: Causal Cascade Updates in Relational Databases with Large Language Models, which introduces a framework for schema-only cascade update generation using large language models. AskDB: An LLM Agent for Natural Language Interaction with Relational Databases, which presents a large language model powered agent designed to bridge the gap between natural language querying and database administration.