Outlier Detection in Serbian CommonCrawl Data

The surge in large language models (LLMs) has greatly advanced natural language processing. However, their development is often hampered by the limited availability and quality of training datasets, particularly for underrepresented languages. Our study aims to address this by enriching Serbian language resources, a crucial step in enhancing LLMs. A key aspect of our methodology is the use of 12 specialized metrics, which allow us to rigorously evaluate the dataset's integrity and reveal linguistic trends in the Serbian digital context. Additionally, we employ a modified Interquartile Range (IQR) method for more stringent outlier detection, aiding in the identification of linguistic anomalies in Serbian text.