# Natural Language Interfaces to Data

arXiv: https://arxiv.org/abs/2212.13074 · HTML: https://mlsanity.com/paper/2212.13074/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2022.

## Built on by (most cited first, 14 of 14)

- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL](https://mlsanity.com/paper/2410.01943/) (2024)
- [The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models](https://mlsanity.com/paper/2408.07702/) (2024)
- [A Universal Question-Answering Platform for Knowledge Graphs](https://mlsanity.com/paper/2303.00595/) (2023)
- [AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots](https://mlsanity.com/paper/2508.02999/) (2025)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [Towards Optimizing SQL Generation via LLM Routing](https://mlsanity.com/paper/2411.04319/) (2024)
- [MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction](https://mlsanity.com/paper/2509.23459/) (2025)
- [End-to-End Text-to-SQL with Dataset Selection: Leveraging LLMs for Adaptive Query Generation](https://mlsanity.com/paper/2508.06387/) (2025)
- [Grading Conversational Responses Of Chatbots](https://mlsanity.com/paper/2303.12038/) (2023)
- [SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation](https://mlsanity.com/paper/2609.08950/) (2026)
- [VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture](https://mlsanity.com/paper/2509.22218/) (2025)
- [Health+: Empowering Individuals via Unifying Health Data](https://mlsanity.com/paper/2602.19319/) (2026)
- [Reliable Answers for Recurring Questions: Boosting Text-to-SQL Accuracy with Template Constrained Decoding](https://mlsanity.com/paper/2604.28028/) (2026)

## Abstract (arXiv)

Recent advances in NLU and NLP have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of non-technical users with quick insights into the data. There are three main challenges in natural language querying (NLQ): (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches for interpreting a user's NLQ. Rule-based systems make use of semantic indices, ontologies, and KGs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning (DL)-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using DL models. Hybrid approaches that utilize both rule-based techniques as well as DL models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot NLQ by exploiting query context between multiple turns of conversation for disambiguation. In this article, we review the background technologies that are used in natural language interfaces, and survey the different approaches to NLQ. We also describe conversational interfaces for data analytics and discuss several benchmarks used for NLQ research and evaluation.