APPLICATION OF OPENAI API FOR SEMANTIC CONTENT ANALYSIS IN INTELLIGENT E-LEARNING SYSTEMS
| dc.contributor.author | Rybak, Mykola | |
| dc.date.accessioned | 2024-02-28T13:51:28Z | |
| dc.date.available | 2024-02-28T13:51:28Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | The thesis aims to describe the practical aspects of using OpenAI API in intelligent e-learning systems. The proposed work contains three main parts. The first part covers a background overview, theoretical aspects of a Large Language Model (LLM), transformer-based Natural Language Processing (NLP) architecture description, and use cases of different transformers. Introduces the term semantic unit and defines its main elements and connection with the capstone project. The second part concerns the rationale of the chosen model’s type for the capstone project and its description and implementation. This part covers requirements for the capstone project, C4 architecture diagrams, an overview of essential modules, libraries, and code examples of the implemented solution leveraging OpenAI API. The final part demonstrates further research on architecture improvements for better semantic content analysis and interaction with intelligent e-learning systems, including a combination of encoder-decoder and embedding models in the solution. | |
| dc.identifier.uri | https://er.auk.edu.ua/handle/234907866/42 | |
| dc.language.iso | en_US | |
| dc.subject | LLM | |
| dc.subject | OpenAI API | |
| dc.subject | semantic unit | |
| dc.subject | Encoder | |
| dc.subject | Decoder | |
| dc.subject | self-attention mechanism | |
| dc.subject | prompt engineering | |
| dc.subject | semantic content analysis | |
| dc.title | APPLICATION OF OPENAI API FOR SEMANTIC CONTENT ANALYSIS IN INTELLIGENT E-LEARNING SYSTEMS | |
| dc.title.alternative | ЗАСТОСУВАННЯ OPENAI API ДЛЯ СЕМАНТИЧНОГО АНАЛІЗУ КОНТЕНТУ В ІНТЕЛЕКТУАЛЬНИХ СИСТЕМАХ ЕЛЕКТРОННОГО НАВЧАННЯ | |
| dc.type | Thesis |