ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL

• URL: http://arxiv.org/abs/2412.10138v1
• Date: Fri, 13 Dec 2024 13:41:18 GMT
• Title: ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
• Authors: Yang Qin, Chao Chen, Zhihang Fu, Ze Chen, Dezhong Peng, Peng Hu, Jieping Ye,
• Abstract summary: We propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2. Our approach begins with multi-task supervised fine-tuning (SFT) using various synthetic training data related tosql generation. We also introduce a Multitask Collaboration Prompting (MCP) strategy to reduce hallucinations duringsql generation.
• Score: 42.019659095480726
• License:
• Abstract: Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g., GPT-4), which limits their applicability in open scenarios. To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2SQL, thereby providing a more practical solution. Our approach begins with multi-task supervised fine-tuning (SFT) using various synthetic training data related to SQL generation. Unlike existing SFT-based Text2SQL methods, we introduced several additional SFT tasks, including schema linking, noise correction, and continuation writing. Engaging in a variety of SQL generation tasks enhances the model's understanding of SQL syntax and improves its ability to generate high-quality SQL queries. Additionally, inspired by the collaborative modes of LLM agents, we introduce a Multitask Collaboration Prompting (MCP) strategy. This strategy leverages collaboration across several SQL-related tasks to reduce hallucinations during SQL generation, thereby maximizing the potential of enhancing Text2SQL performance through explicit multitask capabilities. Extensive experiments and in-depth analyses have been performed on eight open-source LLMs and five widely-used benchmarks. The results demonstrate that our proposal outperforms the latest Text2SQL methods and yields leading performance.

Related papers

• 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)
• SQLfuse: Enhancing Text-to-SQL Performance through Comprehensive LLM Synergy [24.919119901664843]
  This paper introduces a robust system integrating open-source Large Language Models (LLMs) with a suite of tools to enhance query accuracy and usability. demonstrated by its leading performance on the Spider Leaderboard and deployment by Ant Group.
  arXiv  Detail & Related papers  (2024-07-19T06:01:57Z)
• CoE-SQL: In-Context Learning for Multi-Turn Text-to-SQL with Chain-of-Editions [22.493487741249716]
  Large Language Models (LLMs) have been demonstrated to possess impressive capabilities in a variety of domains and tasks. We investigate the issue of prompt design in the multi-turn text-to- task and attempt to enhance the LLMs' reasoning capacity.
  arXiv  Detail & Related papers  (2024-05-04T16:56:14Z)
• SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data [54.69489315952524]
  "Prompt" is designed to improve the few-shot prompting capabilities of Text-to-LLMs. "Prompt" outperforms previous approaches for in-context learning with few labeled data by a large margin. We show that emphPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin.
  arXiv  Detail & Related papers  (2023-11-06T05:24:06Z)
• 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)
• Divide and Prompt: Chain of Thought Prompting for Text-to-SQL [0.03807314298073299]
  Chain-of-thought (CoT) prompting combined with large language models (LLMs) have achieved encouraging results on complex reasoning tasks. We propose Divide-and-Prompt, which first divides the task into subtasks, and then approach each subtask through CoT.
  arXiv  Detail & Related papers  (2023-04-23T06:52:35Z)
• MIGA: A Unified Multi-task Generation Framework for Conversational Text-to-SQL [48.34333725045152]
  Most state-of-the-art conversational text-to-generative methods are incompatible with pre-trained language models (PLMs), such as T5. We present a two-stage unified MultI-task Generation frAmeme (MIGA) that leverages PLMs' ability to tackle conversational text-to-work.
  arXiv  Detail & Related papers  (2022-12-19T07:14:32Z)

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.