StatBot.Swiss: Bilingual Open Data Exploration in Natural Language

• URL: http://arxiv.org/abs/2406.03170v2
• Date: Thu, 6 Jun 2024 08:29:23 GMT
• Title: StatBot.Swiss: Bilingual Open Data Exploration in Natural Language
• Authors: Farhad Nooralahzadeh, Yi Zhang, Ellery Smith, Sabine Maennel, Cyril Matthey-Doret, Raphaël de Fondville, Kurt Stockinger,
• Abstract summary: The potential for improvements brought by Large Language Models (LLMs) in Text-to-Swiss systems is mostly assessed on monolingual English datasets. We release the StatBot, the first benchmark dataset for evaluating Text-to-Swiss systems based on real-world applications.
• Score: 5.149617340100317
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Survey on Employing Large Language Models for Text-to-SQL Tasks [9.527891544418805]
  The increasing volume of data in relational databases pose challenges for users to access and analyze data. Text-to-sql (Text2) solves the issues by utilizing natural language processing (NLP) techniques to convert natural language intosql queries. With the development of Large Language Models (LLMs), a range of LLM-based Text2 methods have emerged.
  arXiv  Detail & Related papers  (2024-07-21T14:48:23Z)
• CodeS: Towards Building Open-source Language Models for Text-to-SQL [42.11113113574589]
  We introduce CodeS, a series of pre-trained language models with parameters ranging from 1B to 15B. CodeS is a fully open language model, which achieves superior accuracy with much smaller parameter sizes. We conduct comprehensive evaluations on multiple datasets, including the widely used Spider benchmark.
  arXiv  Detail & Related papers  (2024-02-26T07:00:58Z)
• Evaluating the Data Model Robustness of Text-to-SQL Systems Based on Real User Queries [4.141402725050671]
  This paper is the first in-depth evaluation of the data model robustness of Text-to-- systems in practice. It is based on a real-world deployment of FootballDB, a system that was deployed over a 9 month period in the context of the FIFA World Cup 2022. All of our data is based on real user questions that were asked live to the system. We manually labeled and translated a subset of these questions for three different data models.
  arXiv  Detail & Related papers  (2024-02-13T10:28:57Z)
• Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval [56.65147231836708]
  We develop SWIM-IR, a synthetic retrieval training dataset containing 33 languages for fine-tuning multilingual dense retrievers. SAP assists the large language model (LLM) in generating informative queries in the target language. Our models, called SWIM-X, are competitive with human-supervised dense retrieval models.
  arXiv  Detail & Related papers  (2023-11-10T00:17:10Z)
• The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants [80.4837840962273]
  We present Belebele, a dataset spanning 122 language variants. This dataset enables the evaluation of text models in high-, medium-, and low-resource languages.
  arXiv  Detail & Related papers  (2023-08-31T17:43:08Z)
• Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [76.76046657162306]
  Large language models (LLMs) have emerged as a new paradigm for Text-to- task. Large language models (LLMs) have emerged as a new paradigm for Text-to- task.
  arXiv  Detail & Related papers  (2023-08-29T14:59:54Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing [48.216386761482525]
  We present MultiSpider, the largest multilingual text-to- schema- dataset which covers seven languages (English, German, French, Spanish, Japanese, Chinese, and Vietnamese) Experimental results under three typical settings (zero-shot, monolingual and multilingual) reveal a 6.1% absolute drop in accuracy in non-English languages. We also propose a simple framework augmentation framework SAVe (Augmentation-with-Verification) which boosts the overall performance by about 1.8% and closes the 29.5% performance gap across languages.
  arXiv  Detail & Related papers  (2022-12-27T13:58:30Z)
• XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing [70.40401197026925]
  In-context learning using large language models has recently shown surprising results for semantic parsing tasks. This work introduces the XRICL framework, which learns to retrieve relevant English exemplars for a given query. We also include global translation exemplars for a target language to facilitate the translation process for large language models.
  arXiv  Detail & Related papers  (2022-10-25T01:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.