🤖 AI Summary
Existing Text-to-SQL methods suffer significant performance degradation on large-scale databases and multi-hop complex queries, while largely overlooking the potential of LLMs to collaboratively leverage external tools and modular models. This paper proposes MAC-SQL, a multi-agent collaborative framework that decouples SQL generation (performed by a primary agent) from sub-database retrieval and SQL error correction (handled by two plug-and-play tool agents), enabling on-demand collaboration and dynamic inter-agent communication. It introduces the first LLM-based multi-agent paradigm for Text-to-SQL, integrating few-shot chain-of-thought reasoning, structured tool invocation, and modular architecture design. Evaluated on the BIRD benchmark, the full MAC-SQL system achieves a 59.59% execution accuracy—setting a new state-of-the-art—while its lightweight variant attains 43.94%, closely approaching the GPT-4 baseline (46.35%).
📝 Abstract
Recent LLM-based Text-to-SQL methods usually suffer from significant performance degradation on"huge"databases and complex user questions that require multi-step reasoning. Moreover, most existing methods neglect the crucial significance of LLMs utilizing external tools and model collaboration. To address these challenges, we introduce MAC-SQL, a novel LLM-based multi-agent collaborative framework. Our framework comprises a core decomposer agent for Text-to-SQL generation with few-shot chain-of-thought reasoning, accompanied by two auxiliary agents that utilize external tools or models to acquire smaller sub-databases and refine erroneous SQL queries. The decomposer agent collaborates with auxiliary agents, which are activated as needed and can be expanded to accommodate new features or tools for effective Text-to-SQL parsing. In our framework, We initially leverage GPT-4 as the strong backbone LLM for all agent tasks to determine the upper bound of our framework. We then fine-tune an open-sourced instruction-followed model, SQL-Llama, by leveraging Code Llama 7B, to accomplish all tasks as GPT-4 does. Experiments show that SQL-Llama achieves a comparable execution accuracy of 43.94, compared to the baseline accuracy of 46.35 for vanilla GPT-4. At the time of writing, MAC-SQL+GPT-4 achieves an execution accuracy of 59.59 when evaluated on the BIRD benchmark, establishing a new state-of-the-art (SOTA) on its holdout test set (https://github.com/wbbeyourself/MAC-SQL).
