Overview of DIMEMEX at IberLEF 2025: Detection of Inappropriate Memes from Mexico

Resumen

This paper presents the overview of the DIMEMEX shared task, organized at IberLEF 2025 and co-located with the 41th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2025). The aim of this task is to promote research on automatic solutions for detecting inappropriate content in memes with a particular focus on Mexican Spanish. Three subtasks were considered: (i) A three-way classification task aimed at determining whether a meme contains hate speech, inappropriate content, or neither; (ii) A fine-grained classification task in which a meme may be categorized into specific hate speech categories; and (iii) A three-way classification, as in (i), restricting participants to exclusively focus on leveraging the use of Large Language Models (LLMs). Participants were provided with a multimodal manual annotated corpus comprising both images and text associated with each meme. As a result, a total of 6 teams out of a total of 10 submissions reported their system descriptions for the final evaluation phase. Results show competitive performance for all subtasks being subtask 1 the one with higher reported results. The data and results are available at https://codalab.lisn.upsaclay.fr/competitions/22012.