We present CaRPS, our system for Track 1 of StanceEval-2026. CaRPS combines a target-conditioned encoder with an LLM-based labeling and verification process to handle targets that are not seen during encoder fine-tuning. The encoder takes both the target and the tweet as input, while the LLM is used to generate labels and verify difficult predictions. These LLM-generated labels can then be used as pseudo-labeled data to further fine-tune the encoder, with the goal of gradually reducing the need for LLM calls. On the development leaderboard, our target-conditioned encoder alone achieves 87.19% Favg2, 76.56% Favg3, and 85.30% accuracy. For the official evaluation, we used adversarial LLM prompting for the target not seen during encoder fine-tuning, achieving 82.89% Favg2, 62.67% Favg3, and 78.98% accuracy. Additional experiments show that adversarial verification improves stance and sarcasm classification over one-shot prompting, while pseudo-label self-training helps adapt the encoder to a held-out target. Overall, CaRPS explores a practical combination of efficient encoder-based prediction and LLM-based labeling and verification for Arabic stance detection.

The SemEval-2025 Task 11 addresses multi-label emotion detection, classifying perceived emotions in text. Our system targets Amharic, a morphologically complex, low-resource language. We fine-tune LaBSE with class-weighted loss for multi-label prediction.Our architecture consists of: (i) text tokenization via LaBSE, (ii) a fully connected layer with sigmoid activation for classification, and (iii) optimization using BCEWithLogitsLoss and AdamW. Ablation studies on class balancing and data augmentation showed that simple upsampling did not improve performance, highlighting the need for more sophisticated techniques.Our system ranked 14th out of 43 teams, achieving 0.4938 accuracy, 0.6931 micro-F1, and 0.6450 macro-F1, surpassing the task baseline (0.6383 macro-F1). Error analysis revealed that anger and disgust were well detected, while fear and surprise were frequently misclassified due to overlapping linguistic cues. Our findings underscore the challenges of multi-label emotion detection in low-resource languages. Future work could explore context-aware embeddings, improved data augmentation, and adaptive loss functions.