Applying Retrieval-Augmented Generation for Academic Discipline Development: Insights from Zero-Shot to Tree-of-Thought Prompting
| Autor: | Polina Shnaider, Anastasiia Chernysheva, Anton Govorov, Maksim Khlopotov, Anna Nikiforova |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 36, Iss 1, Pp 741-747 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2305-7254 2343-0737 |
| DOI: | 10.23919/FRUCT64283.2024.10749916 |
| Popis: | This study assesses the efficiency of large language models (LLMs) in generating university course structures, comparing traditional methods with Retrieval-Augmented Generation (RAG). It involves a comparative analysis across twelve courses using four LLMs: starling-lm-7b-alpha, openchat_3.5, saiga2_13b, and gpt-3.5-turbo, with four distinct prompting approaches. Findings indicate that advanced prompting techniques significantly influence model performance and response variability. The study underscores the importance of selecting appropriate LLMs and prompting strategies to optimize educational outcomes, highlighting RAG's role in enhancing data retrieval accuracy in educational technology. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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