Summary of Mag-sql: Multi-agent Generative Approach with Soft Schema Linking and Iterative Sub-sql Refinement For Text-to-sql, by Wenxuan Xie et al.
MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL
by Wenxuan Xie, Gaochen Wu, Bowen Zhou
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed MAG-SQL framework addresses the limitations of existing In-Context Learning based methods in Text-to-SQL tasks by introducing a multi-agent generative approach with soft schema linking and iterative Sub-SQL refinement. The framework uses entity-based methods to select database columns, novel targets-conditions decomposition, and an iterative generating module comprising Sub-SQL Generator and Refiner. Ablation studies demonstrate the effectiveness of each agent in the MAG-SQL framework. When evaluated on the BIRD benchmark with GPT-4, MAG-SQL achieves an execution accuracy of 61.08%, outperforming vanilla GPT-4 (46.35%) and MAC-SQL (57.56%). Similar progress is made on Spider. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAG-SQL is a new way to answer questions by using a team of agents that work together to find the right answers. This approach helps solve complex questions that involve multiple steps, like breaking down a question into smaller parts and then finding the correct information in a database. The MAG-SQL agents are able to learn from their mistakes and improve over time, which makes them better at answering tricky questions. When tested on a difficult dataset called BIRD, MAG-SQL was able to answer 61% of the questions correctly, which is better than other approaches that were tried. |
Keywords
» Artificial intelligence » Gpt
