We present our solution to Subtask 1 of the Shared Task on the Detection and Classification of Persuasion Techniques in Texts for Slavic Languages. Our approach integrates fine-tuned multilingual transformer models with two complementary robustness-oriented strategies: Walking Embeddings and Content-Debiasing. With the first, we tried to understand the change in embeddings when various manipulation techniques were applied. The latter leverages a supervised contrastive objective over semantically equivalent yet stylistically divergent text pairs, generated via GPT-4. We conduct extensive experiments, including 5-fold cross-validation and out-of-domain evaluation, and explore the impact of contrastive loss weighting.

We present a system for the SlavicNLP 2025 Shared Task on multilabel classification of 25 persuasion techniques across Slavic languages. We investigate the effectiveness of in-context learning with one-shot classification, automatic prompt refinement, and supervised fine-tuning using self-generated annotations. Our findings highlight the potential of LLM-based system to generalize across languages and label sets with minimal supervision.