Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity

• URL: http://arxiv.org/abs/2410.09174v1
• Date: Fri, 11 Oct 2024 18:22:08 GMT
• Title: Context-Aware SQL Error Correction Using Few-Shot Learning -- A Novel Approach Based on NLQ, Error, and SQL Similarity
• Authors: Divyansh Jain, Eric Yang,
• Abstract summary: This paper introduces a novel few-shot learning-based approach for error correction insql generation. It enhances the accuracy of generated queries by selecting the most suitable few-shot error correction examples for a given natural language question (NLQ) In experiments with the open-source dataset, the proposed model offers a 39.2% increase in fixing errors with no error correction and a 10% increase from a simple error correction method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, the demand for automated SQL generation has increased significantly, driven by the need for efficient data querying in various applications. However, generating accurate SQL queries remains a challenge due to the complexity and variability of natural language inputs. This paper introduces a novel few-shot learning-based approach for error correction in SQL generation, enhancing the accuracy of generated queries by selecting the most suitable few-shot error correction examples for a given natural language question (NLQ). In our experiments with the open-source Gretel dataset, the proposed model offers a 39.2% increase in fixing errors from the baseline approach with no error correction and a 10% increase from a simple error correction method. The proposed technique leverages embedding-based similarity measures to identify the closest matches from a repository of few-shot examples. Each example comprises an incorrect SQL query, the resulting error, the correct SQL query, and detailed steps to transform the incorrect query into the correct one. By employing this method, the system can effectively guide the correction of errors in newly generated SQL queries. Our approach demonstrates significant improvements in SQL generation accuracy by providing contextually relevant examples that facilitate error identification and correction. The experimental results highlight the effectiveness of embedding-based selection in enhancing the few-shot learning process, leading to more precise and reliable SQL query generation. This research contributes to the field of automated SQL generation by offering a robust framework for error correction, paving the way for more advanced and user-friendly database interaction tools.

Related papers

• Subtle Errors Matter: Preference Learning via Error-injected Self-editing [59.405145971637204]
  We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE) RISE injects predefined subtle errors into partial tokens of correct solutions to construct hard pairs for error mitigation. Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH.
  arXiv  Detail & Related papers  (2024-10-09T07:43:38Z)
• SQLucid: Grounding Natural Language Database Queries with Interactive Explanations [28.10727203675818]
  SQLucid is a novel user interface that bridges the gap between non-expert users and complex database querying processes. Our code is available at https://github.com/magic-YuanTian/ucid.
  arXiv  Detail & Related papers  (2024-09-10T03:14:09Z)
• 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)
• SQLFixAgent: Towards Semantic-Accurate Text-to-SQL Parsing via Consistency-Enhanced Multi-Agent Collaboration [26.193588535592767]
  We introduce a new consistency-enhanced multi-agent collaborative framework designed for detecting and repairing erroneous SQL. We evaluate our proposed framework on five Text-to-text benchmarks, specifically achieving an improvement of over 3% on the Bird benchmark. Our framework also has a higher token efficiency compared to other advanced methods, making it more competitive.
  arXiv  Detail & Related papers  (2024-06-19T09:57:19Z)
• ProbGate at EHRSQL 2024: Enhancing SQL Query Generation Accuracy through Probabilistic Threshold Filtering and Error Handling [0.0]
  We introduce an entropy-based method to identify and filter out unanswerable results. We experimentally verified that our method can filter unanswerable questions, which can be widely utilized.
  arXiv  Detail & Related papers  (2024-04-25T14:55:07Z)
• Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis [4.412170175171256]
  This study investigates various approaches to using Large Language Models for Text-to- program synthesis. The goal was to input a natural language question along with the database schema and output the correct SELECT query.
  arXiv  Detail & Related papers  (2024-01-22T22:05:42Z)
• 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)
• 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)
• Wav2SQL: Direct Generalizable Speech-To-SQL Parsing [55.10009651476589]
  Speech-to-Spider (S2Spider) aims to convert spoken questions intosql queries given databases. We propose the first direct speech-to-speaker parsing model Wav2 which avoids error compounding across cascaded systems. Experimental results demonstrate that Wav2 avoids error compounding and achieves state-of-the-art results by up to 2.5% accuracy improvement over the baseline.
  arXiv  Detail & Related papers  (2023-05-21T19:26:46Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)

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.