  Cross-lingual emotion detection allows us to analyze global trends, public
opinion, and social phenomena at scale. We participated in the Explainability
of Cross-lingual Emotion Detection (EXALT) shared task, achieving an F1-score
of 0.6046 on the evaluation set for the emotion detection sub-task. Our system
outperformed the baseline by more than 0.16 F1-score absolute, and ranked
second amongst competing systems. We conducted experiments using fine-tuning,
zero-shot learning, and few-shot learning for Large Language Model (LLM)-based
models as well as embedding-based BiLSTM and KNN for non-LLM-based techniques.
Additionally, we introduced two novel methods: the Multi-Iteration Agentic
Workflow and the Multi-Binary-Classifier Agentic Workflow. We found that
LLM-based approaches provided good performance on multilingual emotion
detection. Furthermore, ensembles combining all our experimented models yielded
higher F1-scores than any single approach alone.
