Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models

• URL: http://arxiv.org/abs/2405.06674v1
• Date: Sat, 4 May 2024 15:40:17 GMT
• Title: Open-SQL Framework: Enhancing Text-to-SQL on Open-source Large Language Models
• Authors: Xiaojun Chen, Tianle Wang, Tianhao Qiu, Jianbin Qin, Min Yang,
• Abstract summary: We present a systematic methodology tailored for Text-to-open with open-source coherences. Our contributions include a comprehensive evaluation of open-source LLMs in Text-to-open tasks, the openprompt strategy for effective question representation, and novel strategies for supervised fine-tuning. Remarkably, our method significantly improved Llama2-7B from 2.54% to 41.04% and Code Llama-7B from 14.54% to 48.24% on the BIRD-Dev dataset.
• Score: 15.201658508297333
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite the success of large language models (LLMs) in Text-to-SQL tasks, open-source LLMs encounter challenges in contextual understanding and response coherence. To tackle these issues, we present \ours, a systematic methodology tailored for Text-to-SQL with open-source LLMs. Our contributions include a comprehensive evaluation of open-source LLMs in Text-to-SQL tasks, the \openprompt strategy for effective question representation, and novel strategies for supervised fine-tuning. We explore the benefits of Chain-of-Thought in step-by-step inference and propose the \openexample method for enhanced few-shot learning. Additionally, we introduce token-efficient techniques, such as \textbf{Variable-length Open DB Schema}, \textbf{Target Column Truncation}, and \textbf{Example Column Truncation}, addressing challenges in large-scale databases. Our findings emphasize the need for further investigation into the impact of supervised fine-tuning on contextual learning capabilities. Remarkably, our method significantly improved Llama2-7B from 2.54\% to 41.04\% and Code Llama-7B from 14.54\% to 48.24\% on the BIRD-Dev dataset. Notably, the performance of Code Llama-7B surpassed GPT-4 (46.35\%) on the BIRD-Dev dataset.

Related papers

• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [51.00761167842468]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. benchmarks demonstrate that our approach achieves SOTA execution accuracy among open-source solutions, with 67.2% on BIRD and 87.9% on GPT-4ocorrection. Our approach outperforms a series of GPT-4 based Text-to-Seek systems when adopting DeepSeek (much cheaper) with same intact prompts.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• Synthesizing Text-to-SQL Data from Weak and Strong LLMs [68.69270834311259]
  The capability gap between open-source and closed-source large language models (LLMs) remains a challenge in text-to- tasks. We introduce a synthetic data approach that combines data produced by larger, more powerful models with error information data generated by smaller, not well-aligned models.
  arXiv  Detail & Related papers  (2024-08-06T15:40:32Z)
• 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)
• 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)
• Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm [19.06214756792692]
  In-context learning of large-language models (LLMs) has achieved remarkable success in the field of natural language processing. Case studies reveal that the single-step chain-of-thought approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-correction. A workflow paradigm is proposed, aiming to enhance the attention and problem-solving scope of LLMs through decomposition.
  arXiv  Detail & Related papers  (2024-02-16T13:24:05Z)
• MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL [47.120862170230566]
  Recent Text-to-yourself methods usually suffer from significant performance degradation on "huge" databases. We introduce MAC, a novel Text-to-yourself LLM-based multi-agent collaborative framework. In our framework, we leverage GPT-4 as the strong backbone for all agent tasks to determine the upper bound of our framework. We then fine-tune an open-sourced instruction-followed model,sql-Llama, by leveraging Code 7B, to accomplish all tasks as GPT-4 does.
  arXiv  Detail & Related papers  (2023-12-18T14:40:20Z)
• 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)

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.