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        <title>Cross-lingual Emotion Detection through Large Language Models</title>
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        <namePart type="given">Ram</namePart>
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            <namePart type="given">Orphée</namePart>
            <namePart type="family">De Clercq</namePart>
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        <name type="personal">
            <namePart type="given">Valentin</namePart>
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            <namePart type="given">Shabnam</namePart>
            <namePart type="family">Tafreshi</namePart>
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    <abstract>This paper presents a detailed system description of our entry which finished 1st with a large lead at 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.</abstract>
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    <identifier type="doi">10.18653/v1/2024.wassa-1.44</identifier>
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%0 Conference Proceedings
%T Cross-lingual Emotion Detection through Large Language Models
%A Kadiyala, Ram Mohan Rao
%Y De Clercq, Orphée
%Y Barriere, Valentin
%Y Barnes, Jeremy
%Y Klinger, Roman
%Y Sedoc, João
%Y Tafreshi, Shabnam
%S Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F kadiyala-2024-cross
%X This paper presents a detailed system description of our entry which finished 1st with a large lead at 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.
%R 10.18653/v1/2024.wassa-1.44
%U https://aclanthology.org/2024.wassa-1.44/
%U https://doi.org/10.18653/v1/2024.wassa-1.44
%P 464-469

Markdown (Informal)

[Cross-lingual Emotion Detection through Large Language Models](https://aclanthology.org/2024.wassa-1.44/) (Kadiyala, WASSA 2024)

• Cross-lingual Emotion Detection through Large Language Models (Kadiyala, WASSA 2024)

ACL

• Ram Mohan Rao Kadiyala. 2024. Cross-lingual Emotion Detection through Large Language Models. In Proceedings of the 14th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 464–469, Bangkok, Thailand. Association for Computational Linguistics.