STaR-SQL: Self-Taught Reasoner for Text-to-SQL

• URL: http://arxiv.org/abs/2502.13550v1
• Date: Wed, 19 Feb 2025 08:58:44 GMT
• Title: STaR-SQL: Self-Taught Reasoner for Text-to-SQL
• Authors: Mingqian He, Yongliang Shen, Wenqi Zhang, Qiuying Peng, Jun Wang, Weiming Lu,
• Abstract summary: "chain-of-thought" rationales have proven effective for improving the performance of large language models on complex reasoning tasks. Applying such techniques to structured tasks, such as text-to-driven, remains largely unexplored. In this paper, we introduce Self-Taughter for text-to-driven (STaR-), a novel approach that reframes query generation as a reasoning process. Experimental results on the challenging Spider benchmark demonstrate that STaR- significantly improves text-to-performance, achieving an execution accuracy of 86.6%. These findings underscore the potential of reasoning-augmented training for
• Score: 20.719165038519744
• License:
• Abstract: Generating step-by-step "chain-of-thought" rationales has proven effective for improving the performance of large language models on complex reasoning tasks. However, applying such techniques to structured tasks, such as text-to-SQL, remains largely unexplored. In this paper, we introduce Self-Taught Reasoner for text-to-SQL (STaR-SQL), a novel approach that reframes SQL query generation as a reasoning-driven process. Our method prompts the LLM to produce detailed reasoning steps for SQL queries and fine-tunes it on rationales that lead to correct outcomes. Unlike traditional methods, STaR-SQL dedicates additional test-time computation to reasoning, thereby positioning LLMs as spontaneous reasoners rather than mere prompt-based agents. To further scale the inference process, we incorporate an outcome-supervised reward model (ORM) as a verifier, which enhances SQL query accuracy. Experimental results on the challenging Spider benchmark demonstrate that STaR-SQL significantly improves text-to-SQL performance, achieving an execution accuracy of 86.6%. This surpasses a few-shot baseline by 31.6% and a baseline fine-tuned to predict answers directly by 18.0%. Additionally, STaR-SQL outperforms agent-like prompting methods that leverage more powerful yet closed-source models such as GPT-4. These findings underscore the potential of reasoning-augmented training for structured tasks and open the door to extending self-improving reasoning models to text-to-SQL generation and beyond.

Related papers

• Rationalization Models for Text-to-SQL [13.792561265515003]
  We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-thought model fine-tuning. The process begins with manually annotating a small set of examples, which are then used to prompt a large language model. A rationalization model is subsequently trained on the validated queries, enabling extensive synthetic CoT annotations.
  arXiv  Detail & Related papers  (2025-02-10T18:38:57Z)
• MCTS-SQL: An Effective Framework for Text-to-SQL with Monte Carlo Tree Search [3.521199751827158]
  We present a novel approach to converting natural language queries into database queries. We use Monte Carlo Tree Search (MCTS) and a self-refinement mechanism to enhance accuracy and reliability. Experimental results show that MCTS-IDER achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-01-28T00:52:23Z)
• 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)
• Enhancing LLM Fine-tuning for Text-to-SQLs by SQL Quality Measurement [1.392448435105643]
  Text-to-s enables non-expert users to effortlessly retrieve desired information from databases using natural language queries. Current state-of-the-art (SOTA) models like GPT4 and T5 have shown impressive performance on large-scale benchmarks like BIRD. This paper proposed a novel approach that only needs SQL Quality to enhance Text-to-s performance.
  arXiv  Detail & Related papers  (2024-10-02T17:21:51Z)
• 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)
• DAC: Decomposed Automation Correction for Text-to-SQL [51.48239006107272]
  We introduce De Automation Correction (DAC), which corrects text-to-composed by decomposing entity linking and skeleton parsing. We show that our method improves performance by $3.7%$ on average of Spider, Bird, and KaggleDBQA compared with the baseline method.
  arXiv  Detail & Related papers  (2024-08-16T14:43:15Z)
• RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL [1.734218686180302]
  This paper introduces a method for Text-to- Execute based on Refined Execution Model and Hardness Prompt. It reduces storage and training costs while maintaining performance. Our experiments on the Spider dataset, specifically with large-scale LMs, achieved an exceptional accuracy (EX) of 82.6%.
  arXiv  Detail & Related papers  (2024-06-13T14:04:34Z)
• EPI-SQL: Enhancing Text-to-SQL Translation with Error-Prevention Instructions [0.5755004576310334]
  This paper introduces EPI-, a novel methodological framework leveraging Large Language Models (LLMs) to enhance the performance of Text-to-one tasks. EPI- operates through a four-step process to generate general error-prevention instructions (EPIs) It provides task-specific guidance, enabling the model to circumvent potential errors for the task at hand.
  arXiv  Detail & Related papers  (2024-04-21T03:52:46Z)
• 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)
• Weakly Supervised Text-to-SQL Parsing through Question Decomposition [53.22128541030441]
  We take advantage of the recently proposed question meaning representation called QDMR. Given questions, their QDMR structures (annotated by non-experts or automatically predicted) and the answers, we are able to automatically synthesizesql queries. Our results show that the weakly supervised models perform competitively with those trained on NL- benchmark data.
  arXiv  Detail & Related papers  (2021-12-12T20:02:42Z)

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.