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Improvement of machine leaning algorithms performance by data set dimensionality reduction using cellular automata

A significant challenge in Machine Learning is dealing with high-dimensional data. Complexity knowns as the "curse of dimensionality" results in deterioration оf Machine Learning algorithms performance as the dimensionality and dataset size increases. Cellular automata are a dynamical discrete computational system with mathematical functions knows as rules that result in complex global behaviour. We used one-dimensional elementary cellular automata as a tool for dataset size. Model variables were selected for initial status vector generation and its further transformation to format that is suitable for cellular automata rules application known in cellular automata theory as configuration. Then model iterated through all possible cellular automata rules and various epochs variations were applied. Model performance for reduced dataset was compared with benchmark results of original dataset after standard dimensionality reduction technics used. It was concluded that applied cellular automata rules can be used as alternative methods for dataset size reduction without deteriorating model performance.

Author: Alexey Kuchvalskiy

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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Enhancement Strategies for Retrieval-Augmented Generation Systems

This thesis systematically explores the enhancement of Retrieval-Augmented Generation (RAG) systems within Large Language Models, emphasizing optimization of retrieval parameters and generation accuracy. We investigate optimal configurations in RAG systems, including chunk size and overlap percentages, top-k selection, query transformations, different retrieval methods, different LLMs, namely GPT-3.5-Turbo and GPT-4, discovering that a chunk size of 500 tokens generally offers the best performance. Vector search using cosine similarity emerges as the most effective retrieval method, significantly enhancing both context precision and recall across various tasks and knowledge bases. Experimentation within the CRUD-RAG framework demonstrates its applicability in diverse tasks from content creation to knowledge refinement. Our findings indicate that enhancements in retrieval settings can markedly improve the performance of RAG systems, making them more efficient and adaptable for complex information synthesis and retrieval tasks. These results affirm the potential of systematic enhancements to improve AI-driven language models in practical applications, contributing significant insights and practical approaches to the evolving landscape of RAG system research.

Author: Sigita Lapiņa

Supervisor: Dmitry Pavlyuk

Degree: Master

Year: 2024

Work Language: English

Study programme: Computer Sciences

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