Summary of Rsl-sql: Robust Schema Linking in Text-to-sql Generation, by Zhenbiao Cao et al.
RSL-SQL: Robust Schema Linking in Text-to-SQL Generation
by Zhenbiao Cao, Yuanlei Zheng, Zhihao Fan, Xiaojin Zhang, Wei Chen, Xiang Bai
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 This paper presents a novel framework, RSL-SQL, for text-to-SQL generation that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. The framework addresses the challenges of schema linking by improving recall using forward and backward pruning methods, achieving a strict recall of 94% while reducing input columns by 83%. It also mitigates risks through voting between full and simplified modes enhanced with contextual information. Experimental results on BIRD and Spider benchmarks demonstrate state-of-the-art (SOTA) execution accuracy among open-source solutions, outperforming GPT-4 based Text-to-SQL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers understand natural language questions and turn them into SQL statements. The researchers propose a special framework called RSL-SQL that makes it easier for large language models to get the information they need from databases. They also make sure their method works well by testing it on two important benchmarks. |
Keywords
» Artificial intelligence » Gpt » Pruning » Recall
