Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation

• URL: http://arxiv.org/abs/2308.15363v4
• Date: Mon, 20 Nov 2023 13:59:16 GMT
• Title: Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
• Authors: Dawei Gao, Haibin Wang, Yaliang Li, Xiuyu Sun, Yichen Qian, Bolin Ding, Jingren Zhou
• Abstract summary: 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.
• Score: 76.76046657162306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions. To address this challenge, in this paper, we first conduct a systematical and extensive comparison over existing prompt engineering methods, including question representation, example selection and example organization, and with these experimental results, we elaborate their pros and cons. Based on these findings, we propose a new integrated solution, named DAIL-SQL, which refreshes the Spider leaderboard with 86.6% execution accuracy and sets a new bar. To explore the potential of open-source LLM, we investigate them in various scenarios, and further enhance their performance with supervised fine-tuning. Our explorations highlight open-source LLMs' potential in Text-to-SQL, as well as the advantages and disadvantages of the supervised fine-tuning. Additionally, towards an efficient and economic LLM-based Text-to-SQL solution, we emphasize the token efficiency in prompt engineering and compare the prior studies under this metric. We hope that our work provides a deeper understanding of Text-to-SQL with LLMs, and inspires further investigations and broad applications.

Related papers

• From Natural Language to SQL: Review of LLM-based Text-to-SQL Systems [1.1060425537315088]
  This survey provides a comprehensive study of the evolution of LLM-based text-to-sql systems. We discuss benchmarks, evaluation methods and evaluation metrics. We highlight key challenges such as efficiency, model privacy, and data privacy with perspectives toward their development and improvements in potential areas.
  arXiv  Detail & Related papers  (2024-10-01T20:46:25Z)
• PTD-SQL: Partitioning and Targeted Drilling with LLMs in Text-to-SQL [54.304872649870575]
  Large Language Models (LLMs) have emerged as powerful tools for Text-to-sense tasks. In this study, we propose that employing query group partitioning allows LLMs to focus on learning the thought processes specific to a single problem type.
  arXiv  Detail & Related papers  (2024-09-21T09:33:14Z)
• RB-SQL: A Retrieval-based LLM Framework for Text-to-SQL [48.516004807486745]
  Large language models (LLMs) with in-context learning have significantly improved the performance of text-to- task. We propose RB-, a novel retrieval-based framework for in-context prompt engineering. Experiment results demonstrate that our model achieves better performance than several competitive baselines on public datasets BIRD and Spider.
  arXiv  Detail & Related papers  (2024-07-11T08:19:58Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL [15.75829309721909]
  Generating accuratesql from natural language questions (text-to-) is a long-standing challenge. PLMs have been developed and utilized for text-to- tasks, achieving promising performance. Recently, large language models (LLMs) have demonstrated significant capabilities in natural language understanding.
  arXiv  Detail & Related papers  (2024-06-12T17:13:17Z)
• Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation [33.41556606816004]
  Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to- task. There is still no consensus on the optimal prompt templates and design frameworks. Existing benchmarks inadequately explore the performance of LLMs across the various sub-tasks of the Text-to- process.
  arXiv  Detail & Related papers  (2024-03-05T13:23:48Z)
• 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)
• Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies [20.15851744895469]
  In-context learning (ICL) has emerged as a new approach to various natural language processing tasks. In this paper, we aim to extend this method to question answering tasks that utilize structured knowledge sources.
  arXiv  Detail & Related papers  (2023-05-21T22:44:25Z)

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.