Natural language query to SQL in the era of LLMs: a large language model based experimental approach to break down technical barriers

Cocciò, Edoardo (A.A. 2024/2025) Natural language query to SQL in the era of LLMs: a large language model based experimental approach to break down technical barriers. Tesi di Laurea in Artificial intelligence and machine learning, Luiss Guido Carli, relatore Giuseppe Francesco Italiano, pp. 48. [Bachelor's Degree Thesis]

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Abstract/Index

Background. Problem statement: Text-to-SQL translation challenges. Internship project-Reporting virtual assistant (RVA). Literature review. Evolution of Text-to-SQL translation. Agentic frameworks. Adaptive learning paradigms for Text-to-SQL frameworks. Multi-agent SOTA frameworks. Evaluation metrics. Open-source benchmark datasets. Methodology-introducing RAG-MAC. Research design. Proposed solution. Technologies used. Experiments. Implementing a self-improvement system with (simulated) human feedback. Alternative approaches explored. RAG-evaluation.

References

Bibliografia: pp. 42-44.

Thesis Type: Bachelor's Degree Thesis
Institution: Luiss Guido Carli
Degree Program: Bachelor's Degree Programs > Bachelor's Degree Program in Management and Computer Science, English language (L-18)
Chair: Artificial intelligence and machine learning
Thesis Supervisor: Italiano, Giuseppe Francesco
Academic Year: 2024/2025
Session: Summer
Deposited by: Alessandro Perfetti
Date Deposited: 03 Dec 2025 13:47
Last Modified: 03 Dec 2025 13:47
URI: https://tesi.luiss.it/id/eprint/44179

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