SENSE: Bridging the Gap Between Open-Source and Closed-Source LLMs for Advanced Text-to-SQL Parsing
What are the main concerns associated with closed-source large language models?
The main concerns with closed-source large language models include limited transparency, reduced customizability, potential vendor lock-in, higher costs, and reliance on the provider for updates and support124. These factors can restrict flexibility and control for enterprises, impacting their ability to tailor solutions to specific needs and maintain long-term independence146.
How does the synthetic data approach improve domain generalization in text-to-SQL models?
The synthetic data approach improves domain generalization in text-to-SQL models by combining strong data from powerful models to handle diverse queries and schemas, and weak data from smaller models to learn from errors019. This enhances the model's ability to generalize across various domains and improves robustness078.
What are the key differences between the Spider and BIRD benchmarks?
The Spider benchmark focuses on diverse databases and complex SQL queries, while BIRD is tailored for real business problems, featuring varied databases, standardized query difficulties, and external knowledge138. BIRD presents a greater challenge, with larger performance gaps and a more comprehensive evaluation of LLM-based Text-to-SQL models034.