Framing the Language: Fine-Tuning Gemma 3 for Manipulation Detection
Mykola Khandoga, Yevhen Kostiuk, Anton Polishko, Kostiantyn Kozlov, Yurii Filipchuk, Artur Kiulian · 2025
In this paper, we present our solutions for the two UNLP 2025 shared tasks: manipulation span detection and manipulation technique classification in Ukraine-related media content sourced from Telegram channels.We experimented with fine-tuning large language models (LLMs) with up to 12 billion parameters, including both encoder-and decoderbased architectures.Our experiments identified Gemma 3 12b with a custom classification head as the best-performing model for both tasks.To address the limited size of the original training dataset, we generated 50k synthetic samples and marked up an additional 400k media entries containing manipulative content.