Publication
dfkinit2b at CheckThat! 2025: Leveraging LLMs and Ensemble of Methods for Multilingual Claim Normalization
Tatiana Anikina; Ivan Vykopal; Sebastian Kula; Ravi Kiran Chikkala; Natalia Skachkova; Jing Yang; Veronika Solopova; Vera Schmitt; Simon Ostermann
In: CLEF 2025 Working Notes. Conference and Labs of the Evaluation Forum (CLEF-2025), Information Access Evaluation meets Multilinguality, Multimodality, and Visualization, September 9-12, Madrid, Spain, CEUR Workshop Proceedings, 9/2025.
Abstract
The rapid spread of misinformation on social media across languages presents a major challenge for fact-checking efforts. Social media posts are often noisy, informal, and unstructured, with irrelevant content, making it difficult
to extract concise, verifiable claims. To address this, the CLEF 2025 CheckThat! Shared Task on Multilingual Claim Extraction and Normalization focuses on transforming social media posts into normalized claims, short,
clear and check-worthy statements that capture the essence of potentially misleading content. In this paper, we investigate several approaches to this task, including parameter-efficient fine-tuning, prompting large language
models (LLMs), and an ensemble of methods. We evaluate our approaches in two settings: monolingual, where we are provided with training and validation data, and the zero-shot setting, where no training data is available for
the target language. Our approaches achieved first place in 6 out of 13 languages in the monolingual setting and ranked second or third in the remaining languages. In the zero-shot setting, we achieved the highest performance
across all seven languages, demonstrating strong generalization to unseen languages.