Aug 2026· IPTEK: The Journal for Technology and Science· Vol 37, pp. 100-113· 0 citations
TL;DR
This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.
Abstract
This paper explores the practical implementation of a Chatbot designed to connect natural language communication with database interactions by generating Structured Query Language (SQL) queries. Utilizing the OpenAPI framework, a widely-used specification for building APIs, our approach aims to enhance the flexibility and interoperability of the ChatBot. The system not only interprets user queries expressed in natural language but also transforms them into syntactically correct and semantically meaningful SQL commands. This paper provides an overview of the current landscape of natural language processing (NLP) Chatbots, discusses the architecture and design considerations of our implementation, and highlights the methodology for training and fine-tuning using state-of-the-art NLP models. Real-world use cases are presented to showcase the practical applicability of the Chatbot, demonstrating its ability to handle various complexities inherent in natural language queries. This work contributes to the field by presenting a scalable and robust solution that combines NLP and database interactions through the utilization of OpenAPI, with potential implications for improving human-computer interactions within database systems.
The study empirically demonstrates comparative behaviours of the current LLMs, and suggests the use of execution-aware evaluation in the studies and deployment of Text-to-SQL.
B. N, T. L. Manasa· International journal of com...· 0 citations
Imagine a scenario where we could communicate with a relational query processor through messaging platforms like
WeChat
or
WhatsApp
, asking questions related to query processing and optimization. If realized, such a tool could prove invaluable in database education and administration, among others. In this demonstration, we present ChatQPT, a novel system that enables chat-based interactions with a relational query engine (PostgreSQL). It
combines
an LLM-powered interface with a set of specialized external tools tailored to understand various aspects of relational query processing, effectively addressing the shortcomings of both large language models and off-the-shelf RDBMSs in supporting accurate and effective conversations. Our preliminary evaluation through a user study demonstrates the promising capabilities of ChatQPT.
Hui Li, S. Bhowmick, Bao-Chao Xu et al.· Proceedings of the VLDB Endo...· 0 citations
It is concluded that an NLP- and RAG-based chatbot is feasible to be implemented as a digital campus information service and improves answer accuracy from 58.5% to 89.25%, with an average response time below three seconds.
Zuhri Yanda, Yeni Yanti, Maulinda et al.· IC-ITECHS· 0 citations
Text-to-SQL systems translate natural language questions into executable SQL queries, enabling intuitive access to structured data. While recent large language models have substantially improved generation quality, evaluating these systems remains a complex challenge: SQL semantics are subtle, multiple valid query formulations exist for the same question, and execution-based metrics are implemented inconsistently across the community. We demonstrate Text2SQL-Eval, an open-source, modular framework for rigorous evaluation of text-to-SQL systems. The toolkit provides a comprehensive suite of over twelve metrics, spanning execution accuracy, SQL syntactic equivalence, and LLM-as-judge scoring, together with integrated pipelines for inference, SQL execution against real databases, SQL profiling, and detailed error analysis. A web-based dashboard enables interactive exploration of benchmark results, cross-pipeline comparison, and per-record drill-down with live re-evaluation. The demonstration walks attendees through evaluating and comparing text-to-SQL pipelines on both established public benchmarks and new enterprise benchmarks. Attendees will learn to diagnose failure patterns and use LLM-as-judge to assess predictions where traditional metrics fall short.
Oktie Hassanzadeh, Yotam Perlitz, Nhan H. Pham et al.· Proceedings of the VLDB Endo...· 0 citations
To bridge the semantic gap in NL2SQL (Natural Language to SQL) tasks, this study proposes a "Question→SQL→Wiki" framework that leverages a dynamic Wiki Graph as an intermediate reasoning layer. Departing from conventional NL2SQL approaches that rely solely on end-to-end mapping, our method utilizes Large Language Models (LLMs) to translate natural language questions into SQL queries, subsequently parsing these queries to construct a context-specific Wiki Graph. Unlike static knowledge graphs, this architecture emulates the associative and incremental nature of encyclopedic systems, explicitly modeling both database schemas and query-specific logic. By transforming abstract linguistic intent into a structured, wiki-style representation prior to execution, the framework significantly enhances schema linking and logical correctness. Experiments demonstrate that this graph-augmented paradigm improves the accuracy and robustness of NL2SQL systems, offering a scalable solution for complex query generation over relational databases.
Jia-Xuan Liu, Shi-Yu Fang, Jibing Wu et al.· 2026 12th International Conf...· 0 citations
It is suggested that structured prompt engineering provides a practical alternative to model fine-tuning for locally deployed LLMs, offering an effective balance between SQL generation accuracy, computational efficiency, and data privacy.
Nurjayanti Nurjayanti, A. Adiwijaya, A. Romadhony et al.· Jurnal RESTI (Rekayasa Siste...· 0 citations
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