analiza aiqbench
This paper is a shared-task entry that combines GLiNER entity extraction with a fine-tuned MARBERT classifier to flag harmful Arabic user prompts across seven categories. The reported results are modest on the blind test (accuracy of about 0.79 and 0.77, macro-F1 of about 0.74 and 0.75), and the authors note that distinguishing closely related harm categories remains hard. For readers, the useful takeaway is that adding explicit entity signals to a pretrained Arabic model is a reasonable approach to prompt moderation, but the gains are incremental and the dialect and category-overlap problems are still open.
REGLAT at ArGuard Shared Tasks: An Ensemble-Based Model Integrating NER for Harmful Content Detection in Arabic
arXiv:2610.37265v1 Announce Type: new Abstract: Detecting harmful content in Arabic is challenging because of dialectal variations and the semantic overlap between different harm categories. This paper presents a two-stage approach for classifying Arabic user prompts into seven categories of harmful content. First, GLiNER is used to extract harm-related entities from each prompt. The extracted entities are then added to the original text and passed to a fine-tuned MARBERT classifier for final classification. The approach combines explicit entity information with the contextual understanding of a pretrained Arabic language model. The system was evaluated on the development set of the ArGuard 2026 shared task. The results achieved a macro-F1 score of 0.886 and an accuracy of 0.897, demonstrating promising performance in fine-grained harmful content detection. The system reported an accuracy of 0.793 and Macro-F score of 0.735 on a blind test set for subtask 1. For subtask 2, it achieved an accuracy of 0.770 and Macro-F score of 0.754. The analysis also showed that distinguishing between related categories remains challenging. These findings suggest that integrating NER with Arabic language models is a promising approach to harmful content detection.
Abstract:Detecting harmful content in Arabic is challenging because of dialectal variations and the semantic overlap between different harm categories. This paper presents a two-stage approach for classifying Arabic user prompts into seven categories of harmful content. First, GLiNER is used to extract harm-related entities from each prompt. The extracted entities are then added to the original text and passed to a fine-tuned MARBERT classifier for final classification. The approach combines explicit entity information with the contextual understanding of a pretrained Arabic language model. The system was evaluated on the development set of the ArGuard 2026 shared task. The results achieved a macro-F1 score of 0.886 and an accuracy of 0.897, demonstrating promising performance in fine-grained harmful content detection. The system reported an accuracy of 0.793 and Macro-F score of 0.735 on a blind test set for subtask 1. For subtask 2, it achieved an accuracy of 0.770 and Macro-F score of 0.754. The analysis also showed that distinguishing between related categories remains challenging. These findings suggest that integrating NER with Arabic language models is a promising approach to harmful content detection.
Computation and Language (cs.CL)
arXiv:2610.37265 [cs.CL]
(or arXiv:2610.37265v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.37265 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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