SQLucid: Grounding Natural Language Database Queries with Interactive Explanations

• URL: http://arxiv.org/abs/2409.06178v1
• Date: Tue, 10 Sep 2024 03:14:09 GMT
• Title: SQLucid: Grounding Natural Language Database Queries with Interactive Explanations
• Authors: Yuan Tian, Jonathan K. Kummerfeld, Toby Jia-Jun Li, Tianyi Zhang,
• Abstract summary: 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.
• Score: 28.10727203675818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Though recent advances in machine learning have led to significant improvements in natural language interfaces for databases, the accuracy and reliability of these systems remain limited, especially in high-stakes domains. This paper introduces SQLucid, a novel user interface that bridges the gap between non-expert users and complex database querying processes. SQLucid addresses existing limitations by integrating visual correspondence, intermediate query results, and editable step-by-step SQL explanations in natural language to facilitate user understanding and engagement. This unique blend of features empowers users to understand and refine SQL queries easily and precisely. Two user studies and one quantitative experiment were conducted to validate SQLucid's effectiveness, showing significant improvement in task completion accuracy and user confidence compared to existing interfaces. Our code is available at https://github.com/magic-YuanTian/SQLucid.

Related papers

• E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL [1.187832944550453]
  We introduce E- repository, a novel pipeline designed to address challenges through direct schema linking and candidate predicate augmentation. E- enhances the natural language query by incorporating relevant database items (i.e. tables, columns, and values) and conditions directly into the question, bridging the gap between the query and the database structure. We investigate the impact of schema filtering, a technique widely explored in previous work, and demonstrate its diminishing returns when applied alongside advanced large language models.
  arXiv  Detail & Related papers  (2024-09-25T09:02:48Z)
• Natural Language Query Engine for Relational Databases using Generative AI [0.0]
  This article introduces an innovative solution that leverages Generative AI to bridge the gap, enabling users to query databases using natural language. Our approach automatically translates natural language queries intosql, ensuring both syntactic and semantic correctness, while also generating clear, natural language responses from the retrieved data. By streamlining the interaction between users and databases, this method empowers individuals without technical expertise to engage with data directly and efficiently, democratizing access to valuable insights and enhancing productivity.
  arXiv  Detail & Related papers  (2024-09-23T01:07:02Z)
• 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)
• Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations [31.3376894001311]
  We introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user with 24 participants, demonstrate that our approach can achieve better than multiple SOTA approaches.
  arXiv  Detail & Related papers  (2023-05-12T10:45:29Z)
• 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)
• xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph [0.17188280334580192]
  Translating natural language queries into structured query language (NLQ) in interfaces to relational databases is a challenging task. We propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.
  arXiv  Detail & Related papers  (2022-10-07T18:17:09Z)
• 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)
• 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)
• "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)
• Photon: A Robust Cross-Domain Text-to-SQL System [189.1405317853752]
  We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a mapping cannot be immediately determined. The proposed method effectively improves the robustness of text-to-native system against untranslatable user input.
  arXiv  Detail & Related papers  (2020-07-30T07:44:48Z)

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.