TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection
<|reference_start|>TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection: 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.<|reference_end|>
@article{cheng2024teii:,
    title={TEII: Think, Explain, Interact and Iterate with Large Language Models to
  Solve Cross-lingual Emotion Detection},
    author={Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi, Gina-Anne Levow},
    journal={arXiv preprint arXiv:2405.17129},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.17129},
    primaryClass={cs.CL cs.AI}
}