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The success of ChatGPT has ignited an AI race, with researchers striving todevelop new large language models (LLMs) that can match or surpass the languageunderstanding and generation abilities of commercial ones. In recent times, anumber of models have emerged, claiming performance near that of GPT-3.5 orGPT-4 through various instruction-tuning methods. As practitioners ofText-to-SQL parsing, we are grateful for their valuable contributions toopen-source research. However, it is important to approach these claims with asense of scrutiny and ascertain the actual effectiveness of these models.Therefore, we pit six popular large language models against each other,systematically evaluating their Text-to-SQL parsing capability on ninebenchmark datasets with five different prompting strategies, covering bothzero-shot and few-shot scenarios. Regrettably, the open-sourced models fellsignificantly short of the performance achieved by closed-source models likeGPT-3.5, highlighting the need for further work to bridge the performance gapbetween these models.

Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT -- A Text-to-SQL Parsing Comparison