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Related Experiment Video

Updated: Mar 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Comparative study on the customization of natural language interfaces to databases.

Rodolfo A Pazos R¹, Marco A Aguirre L¹, Juan J González B¹

¹División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Av. 1o. de Mayo s/No., Col. Los Mangos, Ciudad Madero, Tamaulipas Mexico.

|May 19, 2016

Summary

Natural language interfaces to databases (NLIDBs) are complex to customize. A new semantically-enriched data dictionary significantly improved NLIDB performance when customized by end-users, outperforming commercial alternatives.

Keywords:

Databases Natural language interface Natural language processing Semantic modelling

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Natural Language Interfaces to Databases (NLIDBs) are increasingly popular for business decisions.
Customization complexity hinders NLIDB usability and performance for end-users.
Limited research exists on NLIDB performance when customized by non-experts.

Purpose of the Study:

To present a semantically-enriched data dictionary for improved natural language to SQL translation.
To evaluate the performance of a novel NLIDB against a commercial system (ELF) when customized by end-users.
To assess the impact of customization by non-experts on NLIDB accuracy.

Main Methods:

Development of a semantically-enriched data dictionary to address natural language to SQL translation challenges.
An experiment involving two groups of undergraduate students customizing a novel NLIDB and the commercial English Language Frontend (ELF).
Performance evaluation based on the percentage of correctly answered queries on the ATIS database.

Main Results:

Our NLIDB achieved 44.69% and 77.05% accuracy when customized by two different student groups, respectively.
The commercial English Language Frontend (ELF) achieved 11.83% and 13.48% accuracy with the same groups.
Our NLIDB reached 90% accuracy when customized by the implementers.

Conclusions:

The semantically-enriched data dictionary significantly enhances NLIDB performance, especially when customized by end-users.
The developed NLIDB demonstrates superior performance compared to a leading commercial NLIDB when usability and non-expert customization are considered.
Effective customization is crucial for achieving high performance in NLIDBs, and the proposed approach facilitates this for end-users.