XAI has been a long-standing goal of AI. Explaining why a text can be considered to have a dominant narrative, where the narrative is known, is of great importance for dealing with propaganda in news. This paper reports on the participation of the system clujteam in Subtask 3 of Task 10 of Semveal 2025. The system obtained 7th place with a value of 0.72464 for F1macro, at 0.026 distance from the 1st place. The key components of the solution are the011given taxonomy and supervised fine-tuning of SmolLM2.013

In this work, we present our empirical attempt to identify the proper strategy of using Transformer Language Models to identify sentences consistent with commonsense. We tackle the first two tasks from the ComVE competition. The starting point for our work is the BERT assumption according to which a large number of NLP tasks can be solved with pre-trained Transformers with no substantial task-specific changes of the architecture. However, our experiments show that the encoding strategy can have a great impact on the quality of the fine-tuning. The combination between cross-encoding and multi-input models worked better than one cross-encoder and allowed us to achieve comparable results with the state-of-the-art without the use of any external data.