README.md

---
license: cc-by-4.0
language:
- tr
tags:
- Text-to-SQL
- NL2SQL
---

# Dataset Card for TURSpider

[TURSpider](https://github.com/alibugra/TURSpider/) is a human curated variant of the [Spider](https://yale-lily.github.io/spider) Text-to-SQL database.

The source GIT repo for TURSpider is located here: https://github.com/alibugra/TURSpider/


## Paper Abstract

> This paper introduces TURSpider, a novel Turkish Text-to-SQL dataset developed through human translation of the widely used Spider dataset, aimed at addressing the current lack of complex, cross-domain SQL datasets for the Turkish language. TURSpider incorporates a wide range of query difficulties, including nested queries, to create a comprehensive benchmark for Turkish Text-to-SQL tasks. The dataset enables cross-language comparison and significantly enhances the training and evaluation of large language models (LLMs) in generating SQL queries from Turkish natural language inputs. We fine-tuned several Turkish-supported LLMs on TURSpider and evaluated their performance in comparison to state-of-the-art models like GPT-3.5 Turbo and GPT-4. Our results show that fine-tuned Turkish LLMs demonstrate competitive performance, with one model even surpassing GPT-based models on execution accuracy. We also apply the Chain-of-Feedback (CoF) methodology to further improve model performance, demonstrating its effectiveness across multiple LLMs. This work provides a valuable resource for Turkish NLP and addresses specific challenges in developing accurate Text-to-SQL models for low-resource languages.

## Citation Information
```
@ARTICLE{10753591,
  author={Kanburoglu, Ali Bugra and Boray Tek, Faik},
  journal={IEEE Access}, 
  title={TURSpider: A Turkish Text-to-SQL Dataset and LLM-Based Study}, 
  year={2024},
  volume={12},
  number={},
  pages={169379-169387},
  keywords={Training;Structured Query Language;Accuracy;Error analysis;Large language models;Benchmark testing;Cognition;Encoding;Text-to-SQL;LLM;large language models;Turkish;dataset;TURSpider},
  doi={10.1109/ACCESS.2024.3498841}}
```