# NLI4DB: A Systematic Review of Natural Language Interfaces for Databases

**Authors:** Mengyi Liu, Jianqiu Xu  
**arXiv:** [2503.02435](https://arxiv.org/abs/2503.02435)  
**Date:** 2025-03-04  
**Summary page:** https://www.summarizepaper.com/en/paper/2503.02435v1/

## Key points

- Increasing demand for NLIDBs across various domains
- Comprehensive survey of recently proposed NLIDBs
- Translation process from natural language to executable database language in three stages: preprocessing, understanding, and translation
- Contrasting traditional methods with data-driven approaches
- Categorization of natural language understanding methods into rule-based, machine learning-based, and hybrid approaches
- General construction process for executable languages over relational and spatio-temporal databases
- Discussion on benchmarks, evaluation metrics, and methods for generating new benchmarks
- Summary of classification, development, and enhancement of NLIDB systems
- Exploration of deep language understanding techniques related to NLIDB using Large Language Models (LLM)

## Summary

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The increasing demand for querying databases across various domains has led to significant research focus on NLIDBs. The authors provide a comprehensive survey of recently proposed NLIDBs, starting with an introduction to natural language processing techniques, executable database languages, and the intermediate representation bridging natural language and executable language. The translation process from natural language to executable database language is outlined in three stages: natural language preprocessing, understanding, and translation. Traditional methods involving predefined rules, grammars, regular expressions, dependency parsing, and named entity recognition are contrasted with data-driven approaches utilizing large-scale data and machine learning models like word embedding and pattern linking. Natural language understanding methods are categorized into rule-based, machine learning-based, and hybrid approaches. A general construction process for executable languages over relational and spatio-temporal databases is described. The paper also discusses common benchmarks and evaluation metrics for transforming natural language into executable language while exploring methods for generating new benchmarks. Additionally, the classification, development, and enhancement of NLIDB systems are summarized. Deep language understanding techniques related to NLIDB are explored including using Large Language Models (LLM) for Text2SQL tasks, generating natural language interpretations from SQL queries, and transforming speech queries into SQL commands. Overall, the paper provides a detailed overview of the advancements in NLIDBs and their potential applications in enhancing database interaction through natural language interfaces.