Named Entity Recognition (NER) presents specific challenges in Serbian, a morphologically rich language. To address these challenges, a comparative evaluation of distinct model paradigms across diverse text genres was conducted. A rule-based system (SrpNER), a traditional deep learning model (Convolutional Neural Network – CNN), fine-tuned transformer architectures (Jerteh and Tesla), and Large Language Models (LLMs), specifically ChatGPT 4.0 Nano and 4.1 Mini, were evaluated and compared. For the LLMs, a one-shot prompt engineering approach was employed, using prompt instructions aligned with the entity type definitions used in the manual annotation guidelines. Evaluation was performed on three Serbian datasets representing varied domains: newspaper articles, history textbook excerpts, and a sample of literary texts from the srpELTeC collection. The highest performance was consistently achieved by the fine-tuned transformer models, with F1 scores ranging from 0.78 on newspaper articles to 0.96 on primary school history textbook sample.

This study presents the Sentiment Analysis of the Serbian old novels from the 1840-1920 period, employing the Mistral Large Language Model (LLM) to pioneer zero and few-shot learning techniques. The main approach innovates by devising research prompts that include guidance text for zero-shot classification and examples for few-shot learning, enabling the LLM to classify sentiments into positive, negative, or objective categories. This methodology aims to streamline sentiment analysis by limiting responses, thereby enhancing classification precision. Python, along with the Hugging Face Transformers and LangChain libraries, serves as our technological backbone, facilitating the creation and refinement of research prompts tailored for sentence-level sentiment analysis. The results of sentiment analysis in both scenarios, zero-shot and few-shot, have indicated that the zero-shot approach outperforms, achieving an accuracy of 68.2%.