Natural Language Interfaces for Tabular Data Querying and Visualization: A Survey
The emergence of natural language processing has revolutionized the way users interact with tabular data, enabling a shift from traditional query languages and manual plotting to more intuitive, language-based interfaces. The rise of large language models (LLMs) such as ChatGPT and its successors has further advanced this field, opening new avenues for natural language processing techniques. This survey presents a comprehensive overview of natural language interfaces for tabular data querying and visualization, which allow users to interact with data using natural language queries. We introduce the fundamental concepts and techniques underlying these interfaces with a particular emphasis on semantic parsing, the key technology facilitating the translation from natural language to SQL queries or data visualization commands. We then delve into the recent advancements in Text-to-SQL and Text-to-Vis problems from the perspectives of datasets, methodologies, metrics, and system designs. This includes a deep dive into the influence of LLMs, highlighting their strengths, limitations, and potential for future improvements. Through this survey, we aim to provide a roadmap for researchers and practitioners interested in developing and applying natural language interfaces for data interaction in the era of large language models.
Code (0)
등록된 구현이 없습니다.
Tasks
Data InteractionData VisualizationNatural Language QueriesSemantic ParsingSurveyText to SQLText-To-SQLSimilar Papers 제목 키워드 기반
Are We Asking the Right Questions? On Ambiguity in Natural Language Queries for Tabular Data Analysis
Natural language interfaces to tabular data must handle ambiguities inherent to queries. Instead of treating ambiguity as a deficiency, we reframe it as a feature of cooperative interaction where users are intentional ab…
Natural Language QueriesQuestion AnsweringDatabase Search vs. Information Retrieval: A Novel Method for Studying Natural Language Querying of Semi-Structured Data
The traditional approach of querying a relational database is via a formal language, namely SQL. Recent developments in the design of natural language interfaces to databases show promising results for querying either wi…
Information RetrievalNatural Language QueriesRetrievalTableQuery: Querying tabular data with natural language
This paper presents TableQuery, a novel tool for querying tabular data using deep learning models pre-trained to answer questions on free text. Existing deep learning methods for question answering on tabular data have v…
Deep LearningNatural Language QueriesQuestion AnsweringTableQA: Question Answering on Tabular Data
Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analy…
Question AnsweringDBCopilot: Natural Language Querying over Massive Databases via Schema Routing
The development of Natural Language Interfaces to Databases (NLIDBs) has been greatly advanced by the advent of large language models (LLMs), which provide an intuitive way to translate natural language (NL) questions in…
NavigateQuestion GenerationQuestion-GenerationText-To-SQL