Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect

• URL: http://arxiv.org/abs/2208.10099v1
• Date: Mon, 22 Aug 2022 07:18:23 GMT
• Title: Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect
• Authors: Naihao Deng, Yulong Chen, Yue Zhang
• Abstract summary: Text-to-of has attracted attention from both the natural language processing and database communities. We review recent progress on text-to-of for datasets, methods, and evaluation. We hope that this survey can serve as quick access to existing work and motivate future research.
• Score: 12.445150614650801
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Text-to-SQL has attracted attention from both the natural language processing and database communities because of its ability to convert the semantics in natural language into SQL queries and its practical application in building natural language interfaces to database systems. The major challenges in text-to-SQL lie in encoding the meaning of natural utterances, decoding to SQL queries, and translating the semantics between these two forms. These challenges have been addressed to different extents by the recent advances. However, there is still a lack of comprehensive surveys for this task. To this end, we review recent progress on text-to-SQL for datasets, methods, and evaluation and provide this systematic survey, addressing the aforementioned challenges and discussing potential future directions. We hope that this survey can serve as quick access to existing work and motivate future research.

Related papers

• AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries [56.82807063333088]
  We introduce a new benchmark, AMBROSIA, which we hope will inform and inspire the development of text-to-open programs. Our dataset contains questions showcasing three different types of ambiguity (scope ambiguity, attachment ambiguity, and vagueness) In each case, the ambiguity persists even when the database context is provided. This is achieved through a novel approach that involves controlled generation of databases from scratch.
  arXiv  Detail & Related papers  (2024-06-27T10:43:04Z)
• Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL [15.75829309721909]
  Generating accuratesql from natural language questions (text-to-) is a long-standing challenge. PLMs have been developed and utilized for text-to- tasks, achieving promising performance. Recently, large language models (LLMs) have demonstrated significant capabilities in natural language understanding.
  arXiv  Detail & Related papers  (2024-06-12T17:13:17Z)
• Decoupling SQL Query Hardness Parsing for Text-to-SQL [2.30258928355895]
  We introduce an innovative framework for Text-to-coupled based on decoupling query hardness parsing. This framework decouples the Text-to-couple task based on query hardness by analyzing questions and schemas, simplifying the multi-hardness task into a single-hardness challenge.
  arXiv  Detail & Related papers  (2023-12-11T07:20:46Z)
• Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey [30.836162812277085]
  The rise of large language models (LLMs) has further advanced this field, opening new avenues for natural language processing techniques. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements.
  arXiv  Detail & Related papers  (2023-10-27T05:01:20Z)
• 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)
• Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs [89.68522473384522]
  We present Bird, a big benchmark for large-scale database grounded in text-to-efficient tasks. Our emphasis on database values highlights the new challenges of dirty database contents. Even the most effective text-to-efficient models, i.e. ChatGPT, achieves only 40.08% in execution accuracy.
  arXiv  Detail & Related papers  (2023-05-04T19:02:29Z)
• 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)
• Mobile App Tasks with Iterative Feedback (MoTIF): Addressing Task Feasibility in Interactive Visual Environments [54.405920619915655]
  We introduce Mobile app Tasks with Iterative Feedback (MoTIF), a dataset with natural language commands for the greatest number of interactive environments to date. MoTIF is the first to contain natural language requests for interactive environments that are not satisfiable. We perform initial feasibility classification experiments and only reach an F1 score of 37.3, verifying the need for richer vision-language representations.
  arXiv  Detail & Related papers  (2021-04-17T14:48:02Z)
• "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL [49.85635994436742]
  We include human in the loop and present a novel-independent interactive approach (PIIA) that interacts with users using multi-choice questions. PIIA is capable of enhancing the text-to-domain performance with limited interaction turns by using both simulation and human evaluation.
  arXiv  Detail & Related papers  (2020-11-09T02:14:33Z)
• Towards a Natural Language Query Processing System [0.0]
  This paper reports our study on the design and development of a natural language query interface to a backend relational database. The novelty in the study lies in defining a graph database as a middle layer to store necessary metadata needed to transform a natural language query into structured query language. The translation results for some sample queries yielded a 90% accuracy rate.
  arXiv  Detail & Related papers  (2020-09-25T19:52:20Z)

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.