SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation

• URL: http://arxiv.org/abs/2310.18376v4
• Date: Mon, 27 May 2024 17:55:18 GMT
• Title: SQLformer: Deep Auto-Regressive Query Graph Generation for Text-to-SQL Translation
• Authors: Adrián Bazaga, Pietro Liò, Gos Micklem,
• Abstract summary: We introduce a novel Transformer architecture specifically crafted to perform text-to-gressive translation tasks. Our model predicts queries as abstract syntax trees (ASTs) in an autore way, incorporating structural inductive bias in the executable and decoder layers.
• Score: 16.07396492960869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, the task of text-to-SQL translation, which converts natural language questions into executable SQL queries, has gained significant attention for its potential to democratize data access. Despite its promise, challenges such as adapting to unseen databases and aligning natural language with SQL syntax have hindered widespread adoption. To overcome these issues, we introduce SQLformer, a novel Transformer architecture specifically crafted to perform text-to-SQL translation tasks. Our model predicts SQL queries as abstract syntax trees (ASTs) in an autoregressive way, incorporating structural inductive bias in the encoder and decoder layers. This bias, guided by database table and column selection, aids the decoder in generating SQL query ASTs represented as graphs in a Breadth-First Search canonical order. Our experiments demonstrate that SQLformer achieves state-of-the-art performance across six prominent text-to-SQL benchmarks.

Related papers

• Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain [21.593701177605652]
  We propose a Text-to-aware prompting framework, involving a sample and a dynamic revision chain. Our approach incorporates sample demonstrations and fine-grained information related to the given question. To generate executable and accuratesqls without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback.
  arXiv  Detail & Related papers  (2023-07-11T07:16:22Z)
• 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)
• UNITE: A Unified Benchmark for Text-to-SQL Evaluation [72.72040379293718]
  We introduce a UNIfied benchmark for Text-to-domain systems. It is composed of publicly available text-to-domain datasets and 29K databases. Compared to the widely used Spider benchmark, we introduce a threefold increase in SQL patterns.
  arXiv  Detail & Related papers  (2023-05-25T17:19:52Z)
• Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval [17.747079214502673]
  Text-to- is a task that converts a natural language question into a structured query language () to retrieve information from a database. In this paper, we propose an LLM-based framework for Text-to- which retrieves helpful demonstration examples to prompt LLMs. We design a de-semanticization mechanism that extracts question skeletons, allowing us to retrieve similar examples based on their structural similarity.
  arXiv  Detail & Related papers  (2023-04-26T06:02:01Z)
• Towards Generalizable and Robust Text-to-SQL Parsing [77.18724939989647]
  We propose a novel TKK framework consisting of Task decomposition, Knowledge acquisition, and Knowledge composition to learn text-to- parsing in stages. We show that our framework is effective in all scenarios and state-of-the-art performance on the Spider, SParC, and Co. datasets.
  arXiv  Detail & Related papers  (2022-10-23T09:21:27Z)
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions [102.8606542189429]
  The goal of text-to-corpora parsing is to convert a natural language (NL) question to its corresponding structured query language () based on the evidences provided by databases. Deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output query.
  arXiv  Detail & Related papers  (2022-08-29T14:24:13Z)
• S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers [66.78665327694625]
  We propose S$2$, injecting Syntax to question- encoder graph for Text-to- relational parsing. We also employ the decoupling constraint to induce diverse edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used.
  arXiv  Detail & Related papers  (2022-03-14T09:49:15Z)
• 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)
• Natural SQL: Making SQL Easier to Infer from Natural Language Specifications [15.047104267689052]
  We propose an SQL intermediate representation called Natural SQL (Nat) On Spider, a challenging text-to- schema benchmark, we demonstrate that Nat outperforms other IRs, and significantly improves the performance of several previous SOTA models. For existing models that do not support executable generation, Nat easily enables them to generate executable queries, and achieves the new state-of-the-art execution accuracy.
  arXiv  Detail & Related papers  (2021-09-11T01:53:55Z)
• Bertrand-DR: Improving Text-to-SQL using a Discriminative Re-ranker [1.049360126069332]
  We propose a novel discnative re-ranker to improve the performance of generative text-to-rimi models. We analyze relative strengths of the text-to-rimi and re-ranker models for optimal performance. We demonstrate the effectiveness of the re-ranker by applying it to two state-of-the-art text-to-rimi models.
  arXiv  Detail & Related papers  (2020-02-03T04:52:47Z)

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.