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COMPARATIVE ANALYSIS OF LLM-BASED APPROACHES FOR SQL GENERATION

The rapid development of Large Language Models has unlocked opportunities for restructuring software development processes in general as well as in such cases as converting natural language into SQL queries. This study seeks to experimentally evaluate the effects of four LLM-based methods on the efficiency and quality of SQL generation. Evaluation is being held based on following metrics: Correctness, Completeness and Consistency. Studied LLM-based SQL generation methods include Specific LLMs tailored for SQL code generation like SQL Coder frameworks for generating SQL code (Vanna.ai, 2023; Llamaindex, 2023) and Multi agent collaborative networks for transforming language into SQL.The research utilizes a mix of literature review case studies and simulations. It offers a comprehensive review of the advancements in LLM-driven SQL generation encompassing concepts, technologies, methodologies, strengths, limitations, and ethical considerations.This research successfully bridges the gap between theoretical foundations and practical application of AI-augmented approaches while promoting the integration of LLM-based SQL generation, into automated software development processes.

Author: Maksim Ilin

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Assessing the Viability of Natural Language Processing Applications within an Electronic Checklist
System for Freight Forwarders: Rule-based Information Extraction from Cargo Descriptions.

This study investigates the application of Natural Language Processing (NLP) within electronic checklist systems to enhance cargo description and securing practices for freight forwarders. The logistics industry faces significant challenges due to complex and varied legislation and the need for autonomous validation tools for cargo securing. This research aims to develop a rule-based Named Entity Recognition (NER) model to standardize and automate the extraction of entities from cargo descriptions. Key components of this study include the development of an entity extraction mechanism using regular expressions and standardized codes. The research demonstrates the potential of NLP solutions to generate precise, dynamic checklists from detailed cargo descriptions, ensuring that all pertinent tasks are covered. The developed NER model's effectiveness is evaluated through a series of experiments, showcasing high precision, recall, and F1 scores, thus highlighting its practical applicability in real-world logistics operations. The findings underscore the importance of standardizing cargo-related information to facilitate the broader adoption of automated NLP solutions in the logistics industry.

Author: Nikita Mickevičs

Supervisor: Dmitry Pavlyuk

Degree: Professional Bachelor

Year: 2024

Work Language: English

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