V-SQL: A View-based Two-stage Text-to-SQL Framework

• URL: http://arxiv.org/abs/2502.15686v1
• Date: Tue, 17 Dec 2024 02:27:50 GMT
• Title: V-SQL: A View-based Two-stage Text-to-SQL Framework
• Authors: Zeshun You, Jiebin Yao, Dong Cheng, Zhiwei Wen, Zhiliang Lu, Xianyi Shen,
• Abstract summary: Text-to-coupling methods based on large language models (LLMs) have garnered significant attention.<n>The core of mainstream text-to-coupling frameworks is schema linking, which aligns user queries with relevant tables and columns in the database.<n>Previous methods focused on schema linking while to enhance LLMs' understanding of database schema.
• Score: 0.9719868595277401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The text-to-SQL task aims to convert natural language into Structured Query Language (SQL) without bias. Recently, text-to-SQL methods based on large language models (LLMs) have garnered significant attention. The core of mainstream text-to-SQL frameworks is schema linking, which aligns user queries with relevant tables and columns in the database. Previous methods focused on schema linking while neglecting to enhance LLMs' understanding of database schema. The complex coupling relationships between tables in the database constrain the SQL generation capabilities of LLMs. To tackle this issue, this paper proposes a simple yet effective strategy called view-based schema. This strategy aids LLMs in understanding the database schema by decoupling tightly coupled tables into low-coupling views. We then introduce V-SQL, a view-based two-stage text-to-SQL framework. V-SQL involves the view-based schema strategy to enhance LLMs' understanding of database schema. Results on the authoritative datasets Bird indicate that V-SQL achieves competitive performance compared to existing state-of-the-art methods.

Related papers

• PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics for Text-to-SQL [8.416319689644556]
  It is challenging to convert tasks due to the vast number of database schemas with redundancy.<n>We propose a progressive schema linking with multi-granularity semantics (PSM-)<n>PSM- learns the schema semantics at the column, table, and database levels.
  arXiv  Detail & Related papers  (2025-02-07T08:31:57Z)
• Extractive Schema Linking for Text-to-SQL [17.757832644216446]
  Text-to-one is emerging as a practical interface for real world databases. We introduce a new approach to adapt decoder-only LLMs to schema linking.
  arXiv  Detail & Related papers  (2025-01-23T19:57:08Z)
• 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)
• 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)
• PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency [19.067737007347613]
  Methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%. Our methods achieve new SOTA results on the Spider benchmark, with an execution accuracy of 87.6%.
  arXiv  Detail & Related papers  (2024-03-13T02:32:41Z)
• Schema-Aware Multi-Task Learning for Complex Text-to-SQL [4.913409359995421]
  We present a schema-aware multi-task learning framework (named MT) for complicatedsql queries. Specifically, we design a schema linking discriminator module to distinguish the valid question-schema linkings. On the decoder side, we define 6-type relationships to describe the connections between tables and columns.
  arXiv  Detail & Related papers  (2024-03-09T01:13:37Z)
• An In-Context Schema Understanding Method for Knowledge Base Question Answering [70.87993081445127]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. We propose a simple In-Context Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning.
  arXiv  Detail & Related papers  (2023-10-22T04:19:17Z)
• 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)
• STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing [64.80483736666123]
  We propose a novel pre-training framework STAR for context-dependent text-to- parsing. In addition, we construct a large-scale context-dependent text-to-the-art conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks.
  arXiv  Detail & Related papers  (2022-10-21T11:30:07Z)
• Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph [6.13728903057727]
  The generalizability to new databases is of vital importance to Text-to- systems which aim to parse human utterances intosql statements. In this paper, we propose a framework named IS ESL to iteratively build a enhanced semantic schema-linking graph between question tokens and database schemas. Extensive experiments on three benchmarks demonstrate that IS ESL could consistently outperform the baselines and further investigations show its generalizability and robustness.
  arXiv  Detail & Related papers  (2022-08-08T03:59:33Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)

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.