This paper presents an overview of the Shared Task organized in the First Workshop on Multilingual Counterspeech Generation at COLING 2025. While interest in automatic approaches to Counterspeech generation has been steadily growing, the large majority of the published experimental work has been carried out for English. This is due to the scarcity of both non-English manually curated training data and to the crushing predominance of English in the generative Large Language Models (LLMs) ecosystem. The task’s goal is to promote and encourage research on Counterspeech generation in a multilingual setting (Basque, English, Italian, and Spanish) potentially leveraging background knowledge provided in the proposed dataset. The task attracted 11 participants, 9 of whom presented a paper describing their systems. Together with the task, we introduce a new multilingual counterspeech dataset with 2384 triplets of hate speech, counterspeech, and related background knowledge covering 4 languages. The dataset is available at: https://huggingface.co/datasets/LanD-FBK/ML_MTCONAN_KN.

This paper describes the automated generation of CounterNarratives (CNs) for Hate Speech (HS) in Spanish using GPT-based models. Our primary objective is to evaluate the performance of these models in comparison to human capabilities. For this purpose, the English CONAN Multitarget corpus is taken as a starting point and we use the DeepL API to automatically translate into Spanish. Two GPT-based models, GPT-3 and GPT-4, are applied to the HS segment through a few-shot prompting strategy to generate a new CN. As a consequence of our research, we have created a high quality corpus in Spanish that includes the original HS-CN pairs translated into Spanish, in addition to the CNs generated automatically with the GPT models and that have been evaluated manually. The resulting CONAN-MT-SP corpus and its evaluation will be made available to the research community, representing the most extensive linguistic resource of CNs in Spanish to date. The results demonstrate that, although the effectiveness of GPT-4 outperforms GPT-3, both models can be used as systems to automatically generate CNs to combat the HS. Moreover, these models consistently outperform human performance in most instances.