XGBoost beat LLMs at finding civilian-harm posts in Ukraine war Telegram data

In a recent analysis, XGBoost has outperformed large language models (LLMs) in identifying posts related to civilian harm within Telegram data from the Ukraine war. This finding highlights the potential of traditional machine learning techniques in specific applications, particularly in conflict monitoring and humanitarian efforts. For more details on this study, visit the full article at Bellingcat.