Paper: DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

[konabuta]

DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction

Summary by Copilot

• DIN-SQL stands for Decomposed In-Context Learning of Text-to-SQL with Self-Correction. It is a method that breaks down the text-to-SQL task into multiple steps and feeds the intermediate results to LLMs to improve their performance and accuracy¹².
• Text-to-SQL is a challenging task that aims to translate natural language utterances into SQL queries that can be executed on a relational database. For example, given the utterance "find employees who make more than their managers" and the schema of tables, one may want to generate a query in SQL that retrieves those employees from a database¹².
• LLMs are pre-trained neural models that can generate natural language and code based on a given context or prompt. Examples of LLMs are GPT-4, CodeX, and BART¹². LLMs can perform text-to-SQL tasks using zero-shot or few-shot prompting, which means they do not require any fine-tuning or additional training data¹².
• Self-Correction is a technique that allows LLMs to correct their own mistakes by comparing the generated SQL query with the natural language utterance and the database schema. If there is a mismatch, the LLM can revise the query until it matches the intended meaning¹².
• The paper claims that DIN-SQL outperforms existing methods on two text-to-SQL benchmarks: Spider and BIRD. On the Spider dataset, DIN-SQL achieves an execution accuracy of 85.3%, surpassing the previous state-of-the-art of 79.9%. On the BIRD dataset, DIN-SQL achieves an execution accuracy of 55.9%, setting a new state-of-the-art¹².
• The paper was submitted to NeurIPS 2023, a prestigious conference on neural information processing systems¹. The paper is available on arXiv¹ and GitHub³.

Summary: DIN-SQL is a novel method for generating SQL queries from natural language using large language models and self-correction. It achieves state-of-the-art results on two text-to-SQL benchmarks. The paper is submitted to NeurIPS 2023 and can be accessed online.

reference

Source: Conversation with Bing, 2/28/2024 ([1](https://arxiv.org/abs/2304.11015)) DIN-SQL: Decomposed In-Context Learning of Text-to-SQL .... https://arxiv.org/abs/[2](https://ar5iv.labs.arxiv.org/html/2304.11015)304.11015. (2) DIN-SQL: Decomposed In-Context Learning of Text-to-SQL .... https://ar5iv.labs.arxiv.org/html/2304.[1](https://arxiv.org/abs/2304.11015)1015. (3) GitHub - madhup-google/DIN-SQL: https://arxiv.org/pdf/[2](https://ar5iv.labs.arxiv.org/html/2304.11015)304. .... https://github.com/madhup-google/DIN-SQL. (4) DIN-SQL: Decomposed In-Context Learning of Text-to-SQL .... https://deepai.org/publication/din-sql-decomposed-in-context-learning-of-text-to-sql-with-self-correction. (5) undefined. https://doi.org/[1](https://arxiv.org/abs/2304.11015)0.48550/arXiv.[2](https://ar5iv.labs.arxiv.org/html/2304.11015)304.[1](https://arxiv.org/abs/2304.11015)1015. (6) undefined. https://arxiv.org/pdf/[2](https://ar5iv.labs.arxiv.org/html/2304.11015)304. (7) undefined. https://arxiv.org/pdf/2304.[1](https://arxiv.org/abs/2304.11015)[1](https://arxiv.org/abs/2304.11015)0[1](https://arxiv.org/abs/2304.11015)5.pdf.

[konabuta]

Spider dataset

• Use two official evaluation metrics
  • execution accuracy (EX)
  • exact set match accuracy (EM)
• Models: Codex family (Davinci and Cushman), and GPT-4

[konabuta]

Bird Benchmark

• introduced valid efficiecy score (VES)

[konabuta]

Methodology

1. Schema Linking
2. Query Classification and decomposition
3. SQL generation
4. Self-correction

[konabuta]

Schema Linking Module

• Identifying references to database schema and condition values in natural language queries.

[konabuta]

Classification & Decomposition Module

• Challenge: Select correct table to be joined.
• Classify each query into "easy", "non-nested complex", and "nested complex".
• Detect the set of tables to b e joined.

[konabuta]

SQL Generation Module

• easy class
  • a simple few-shot prompting with no intermediate steps
• non-nested complex class (require join)
  • use NatSQL

Self-correction Module

• Challenge: missing or redundant keywords (DESC, DISTINCT, and agg func)
• generic
  • identify and correct the error in buggy sql (generated sql?)
• gentle
  • not assume the sql query is buggy.
  • ask model to check for any potential issues and provides some hints on the clauses to be checked.