In this paper, we describe the Gradient Descenders submission to SemEval-2026 Task 9 Subtask 2: Multi-Label Hate Speech Detection. Existing Transformer-based approaches often exhibit degraded performance on this task due to severe class imbalance and complex class intersectionality, leading to the learning of spurious correlations. To counteract this, we introduce a novel, data-centric counterfactual augmentation pipeline. We employ Large Language Models (LLMs) as semantic generators to synthesize diverse, targeted training samples via three distinct prompting strategies: Additive Label-Flipping (Attribute Injection), Context Decoupling, and Cross-Domain Identity Substitution. Fine-tuning a RoBERTa classifier on this augmented corpus significantly improves the model’s sensitivity to minority classes. Ultimately, our system achieves a Macro-F1 score of 44.15% on the official test set, highlighting the efficacy of targeted LLM-based augmentation in highly imbalanced, multi-label environments.

This paper presents the Gradient Descenders system for DialectSentEval Subtask 1 on multi-dialect Arabic sentiment analysis. Because general-purpose models often miss dialectal nuances, we compare three pretrained encoders with distinct linguistic orientations: MARBERTv2, DarijaBERT, and AraBERT. We formulate the task as three-class classification, employing a parameter-efficient head-only adaptation strategy that freezes encoder parameters. Our results show MARBERTv2 outperforms the others, achieving a Macro F1 of 80.11% and Accuracy of 81.90%, securing fifth place overall. These findings emphasize that backbone selection is critical; specifically, broad dialect-oriented pretraining yields the most transferable representations for multi-dialect generalization.