Large Language Models for Cross-lingual Emotion Detection
This work addresses emotion detection across languages, but it appears incremental as it applies existing LLM methods to a specific competition task.
The authors tackled cross-lingual emotion detection by using ensembles of large language models, achieving performance that significantly outperformed other submissions in the WASSA 2024 Task 2.
This paper presents a detailed system description of our entry for the WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilized a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages. Our approach not only outperformed other submissions with a large margin, but also demonstrated the strength of integrating multiple models to enhance performance. Additionally, We conducted a thorough comparison of the benefits and limitations of each model used. An error analysis is included along with suggested areas for future improvement. This paper aims to offer a clear and comprehensive understanding of advanced techniques in emotion detection, making it accessible even to those new to the field.