Large Language Models for Cross-lingual Emotion Detection

• URL: http://arxiv.org/abs/2410.15974v1
• Date: Mon, 21 Oct 2024 13:00:09 GMT
• Title: Large Language Models for Cross-lingual Emotion Detection
• Authors: Ram Mohan Rao Kadiyala,
• Abstract summary: This paper presents a detailed system description of our entry for the WASSA 2024 Task 2, focused on cross-lingual emotion detection. We utilize a combination of large language models (LLMs) and their ensembles to effectively understand and categorize emotions across different languages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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