Summary of Magic: Generating Self-correction Guideline For In-context Text-to-sql, by Arian Askari et al.
MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL
by Arian Askari, Christian Poelitz, Xinye Tang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Human-Computer Interaction (cs.HC)
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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 MAGIC method automates the creation of self-correction guidelines for large language models (LLMs) in text-to-SQL tasks. This novel multi-agent approach uses three specialized agents: a manager, correction, and feedback agent to iteratively generate and refine a guideline tailored to LLM mistakes. The agents collaborate on the training set failures to produce a self-correction guideline that outperforms expert human-created ones. The MAGIC method enhances interpretability of corrections made, providing insights into analyzing the reason behind LLM successes and failures in self-correction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MAGIC is a way to help big language models learn from their mistakes. Right now, people need to make rules for correcting these mistakes by hand. This can be hard and not perfect. MAGIC makes it easier by having three computers work together. They use the mistakes made by the language model on some practice data to create a set of rules that are good at fixing those mistakes. This helps humans understand why the language model is making certain errors or successes. |
Keywords
» Artificial intelligence » Language model
