StatBot.Swiss: Bilingual Open Data Exploration in Natural Language
Farhad Nooralahzadeh, Yi Zhang, Ellery Smith, Sabine Maennel, Cyril Matthey-Doret, Raphaël De Fondeville, Kurt Stockinger
Abstract
The potential for improvements brought by Large Language Models (LLMs) in Text-to-SQL systems is mostly assessed on monolingual English datasets. However, LLMs’ performance for other languages remains vastly unexplored. In this work, we release the StatBot.Swiss dataset, the first bilingual benchmark for evaluating Text-to-SQL systems based on real-world applications. The StatBot.Swiss dataset contains 455 natural language/SQL-pairs over 35 big databases with varying level of complexity for both English and German.We evaluate the performance of state-of-the-art LLMs such as GPT-3.5-Turbo and mixtral-8x7b-instruct for the Text-to-SQL translation task using an in-context learning approach. Our experimental analysis illustrates that current LLMs struggle to generalize well in generating SQL queries on our novel bilingual dataset.- Anthology ID:
- 2024.findings-acl.326
- Volume:
- Findings of the Association for Computational Linguistics ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand and virtual meeting
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5486–5507
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.326
- DOI:
- 10.18653/v1/2024.findings-acl.326
- Cite (ACL):
- Farhad Nooralahzadeh, Yi Zhang, Ellery Smith, Sabine Maennel, Cyril Matthey-Doret, Raphaël De Fondeville, and Kurt Stockinger. 2024. StatBot.Swiss: Bilingual Open Data Exploration in Natural Language. In Findings of the Association for Computational Linguistics ACL 2024, pages 5486–5507, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
- Cite (Informal):
- StatBot.Swiss: Bilingual Open Data Exploration in Natural Language (Nooralahzadeh et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/2024.findings-acl.326.pdf