Summary of Teii: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection, by Long Cheng et al.
TEII: Think, Explain, Interact and Iterate with Large Language Models to Solve Cross-lingual Emotion Detection
by Long Cheng, Qihao Shao, Christine Zhao, Sheng Bi, Gina-Anne Levow
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research paper presents a study in cross-lingual emotion detection, which enables the analysis of global trends, public opinion, and social phenomena at scale. The authors participated in the EXALT shared task, achieving an F1-score of 0.6046 on the evaluation set for the emotion detection sub-task, outperforming the baseline by more than 0.16 F1-score absolute and ranking second amongst competing systems. The study explores LLM-based models, fine-tuning, zero-shot learning, few-shot learning, BiLSTM, KNN, and ensemble methods to develop novel approaches in multilingual emotion detection. The results indicate that LLM-based approaches provide good performance on this task, with ensembles combining all experimented models yielding higher F1-scores than any single approach alone. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cross-lingual emotion detection lets us study global trends, public opinion, and social phenomena at a massive scale. Researchers did well in a shared task called EXALT, getting an F1-score of 0.6046 on the evaluation set for detecting emotions. They tried different approaches like fine-tuning, zero-shot learning, and few-shot learning using Large Language Model (LLM) models as well as other methods like BiLSTM and KNN. They also came up with two new ideas: the Multi-Iteration Agentic Workflow and the Multi-Binary-Classifier Agentic Workflow. The results show that LLM-based approaches work well for detecting emotions in many languages, and combining all their tried-and-tested models gives even better results. |
Keywords
» Artificial intelligence » F1 score » Few shot » Fine tuning » Large language model » Zero shot
