Alexandra Fodor – The potential applications of large language models in text analysis annotation

2025 Survey Statistics and Data Analytics MSc Supervisor Eszter Katona

Alexandra Fodor

The thesis examines the applicability of generative large language models in text analysis annotation tasks using a corpus of texts related to depression. The research compares the performance of the closed-source GPT-4o mini and the open-source Llama 3.3 70B, comparing the results of zero-shot and few-shot techniques for both models. In terms of accuracy, the few-shot approach led to a slight improvement over the zero-shot technique. Overall, the Llama model performed slightly better than GPT. The two models performed moderately in terms of accuracy, but their consistency and reliability can be considered high.

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