Review of LLM based Text-to-SQL Application
Natural language interfaces to databases are gaining traction as a way to make data access more intuitive. Text-to-SQL systems aim to automatically translate natural language questions into executable SQL queries. This could allow non-technical users to query databases through conversational interfaces.
However, generating semantically accurate SQL from free-form questions remains challenging. Traditional NLP models struggle to fully understand questions and produce valid SQL code. But the rise of large language models (LLMs) like GPT-3 is changing the landscape. Their few-shot learning capacity shows promise on Text-to-SQL tasks.
Still, LLMs need careful prompt engineering to excel at this specialized domain. As evidenced by results on the Spider benchmark, they lag behind finely tuned models on complex queries. So work is needed to tailor prompting strategies based on query complexity. The DIN-SQL system does this with a decomposed prompt design and achieves state-of-the-art 85.3% execution accuracy on Spider.
Another issue is verifying the accuracy of generated SQL. For language tasks, we care about semantic correctness. But for Text-to-SQL, the SQL must execute and return the expected result set. So additional logic is required to check query accuracy, not just rely on the LLM.
Most research uses open datasets like Spider for development. But performance on real-world business datasets with larger, more complex schemas remains relatively underexplored. Spider queries also tend to use simpler vocabulary than users might. So further work is needed to handle business domains.
Nonetheless, LLMs’ few-shot learning capacity makes them a tantalizing option for Text-to-SQL moving forward. With customized prompting strategies and accuracy verification, they could soon offer conversational SQL querying out-of-the-box. That would greatly expand access to data analytics for non-technical users. The future is bright for natural language interfaces to databases powered by LLMs!