Interactive Text-to-SQL Generation via Editable Step-by-Step
Explanations
- URL: http://arxiv.org/abs/2305.07372v5
- Date: Thu, 4 Jan 2024 23:54:41 GMT
- Title: Interactive Text-to-SQL Generation via Editable Step-by-Step
Explanations
- Authors: Yuan Tian, Zheng Zhang, Zheng Ning, Toby Jia-Jun Li, Jonathan K.
Kummerfeld, Tianyi Zhang
- Abstract summary: 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.
- Score: 31.3376894001311
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Relational databases play an important role in business, science, and more.
However, many users cannot fully unleash the analytical power of relational
databases, because they are not familiar with database languages such as SQL.
Many techniques have been proposed to automatically generate SQL from natural
language, but they suffer from two issues: (1) they still make many mistakes,
particularly for complex queries, and (2) they do not provide a flexible way
for non-expert users to validate and refine incorrect queries. To address these
issues, 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 study with 24 participants, demonstrate
that our approach can achieve better performance than multiple SOTA approaches.
Our code and datasets are available at https://github.com/magic-YuanTian/STEPS.
Related papers
- On Repairing Natural Language to SQL Queries [2.5442795971328307]
We analyze when text-to- tools fail to return the correct query.
It is often the case that the returned query is close to a correct query.
We propose to repair these failing queries using a mutation-based approach.
arXiv Detail & Related papers (2023-10-05T19:50:52Z) - 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) - Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play [46.07002748587857]
We explore augmenting the training datasets using self-play, which leverages contextual information to synthesize new interactions.
We find that self-play improves the accuracy of a strong baseline on SParC and Co, two widely used text-to-domain datasets.
arXiv Detail & Related papers (2022-10-21T16:40:07Z) - AskYourDB: An end-to-end system for querying and visualizing relational
databases using natural language [0.0]
We propose a semantic parsing approach to address the challenge of converting complex natural language into SQL.
We modified state-of-the-art models, by various pre and post processing steps which make the significant part when a model is deployed in production.
To make the product serviceable to businesses we added an automatic visualization framework over the queried results.
arXiv Detail & Related papers (2022-10-16T13:31:32Z) - Deep Learning Driven Natural Languages Text to SQL Query Conversion: A
Survey [2.309914459672557]
In this paper, we try to present a holistic overview of 24 recent neural network models studied in the last couple of years.
We also give an overview of 11 datasets that are widely used to train models for TEXT2 technologies.
arXiv Detail & Related papers (2022-08-08T20:54:34Z) - Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open
Domain Question Answering [78.9863753810787]
A large amount of world's knowledge is stored in structured databases.
query languages can answer questions that require complex reasoning, as well as offering full explainability.
arXiv Detail & Related papers (2021-08-05T22:04:13Z) - "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.