The task of persuasion techniques detection is limited by several challenges, such as insufficient training data and ambiguity in labels. In this paper, we describe a solution for the Slavic NLP 2025 Shared Task. It utilizes multilingual XLM-RoBERTa, that was trained on 100 various languages, and Slavic BERT, a model fine-tuned on four languages of the Slavic group. We suggest to augment the training dataset with related data from previous shared tasks, as well as some automatic translations from English and German. The resulting solutions are ranked among the top 3 for Russian in the Subtask 1 and for all languages in the Subtask 2. We release the code for our solution - https://github.com/ssenichev/ACL_SlavicNLP2025.

We present our submission to SciHal Subtask 1: coarse-grained hallucination detection for scientific question answering. We frame hallucination detection as an NLI-style three-way classification (entailment, contradiction, unverifiable) and show that simple fine-tuning of NLI-adapted encoder models on task data outperforms more elaborate feature-based pipelines and large language model prompting. In particular, DeBERTa-V3-large, a model pretrained on five diverse NLI corpora, achieves the highest weighted F1 on the public leaderboard. We additionally explore a pipeline combining joint claim–reference embeddings and NLI softmax probabilities fed into a classifier, but find its performance consistently below direct encoder fine-tuning. Our findings demonstrate that, for reference-grounded hallucination detection, targeted encoder fine-tuning remains the most accurate and efficient approach.

The task of generating long narratives using Large Language Models (LLMs) is a largely unexplored area within natural language processing (NLP). Although modern LLMs can handle up to 1 million tokens, ensuring coherence and control over long story generation is still a significant challenge. This paper investigates the use of summarization techniques to create extended narratives, specifically targeting long stories. We propose a special prompting scheme that segments the narrative into several parts and chapters, each generated iteratively with contextual information. Our approach is evaluated with GAPELMAPER, a sophisticated text coherence metric, for automatic evaluation to maintain the structural integrity of the generated stories. We also rely on human evaluation to assess the quality of the generated text. This research advances the development of tools for long story generation in NLP, highlighting both the potential and current limitations of LLMs in this field.