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    <abstract>Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts.</abstract>
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%0 Conference Proceedings
%T Lazarus NLP at SemEval-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing
%A Wongso, Wilson
%A Setiawan, David
%A Joyoadikusumo, Ananto
%A Limcorn, Steven
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F wongso-etal-2025-lazarus
%X Multi-label emotion classification in low-resource languages remains challenging due to limited annotated data and model adaptability. To address this, we fine-tune large language models (LLMs) using a sentence-label pairing approach, optimizing efficiency while improving classification performance. Evaluating on Sundanese, Indonesian, and Javanese, our method outperforms conventional classifier-based fine-tuning and achieves strong zero-shot cross-lingual transfer. Notably, our approach ranks first in the Sundanese subset of SemEval-2025 Task 11 Track A. Our findings demonstrate the effectiveness of LLM fine-tuning for low-resource emotion classification, underscoring the importance of tailoring adaptation strategies to specific language families in multilingual contexts.
%U https://aclanthology.org/2025.semeval-1.104/
%P 763-772

Markdown (Informal)

[Lazarus NLP at SemEval-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing](https://aclanthology.org/2025.semeval-1.104/) (Wongso et al., SemEval 2025)

• Lazarus NLP at SemEval-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing (Wongso et al., SemEval 2025)

ACL

• Wilson Wongso, David Setiawan, Ananto Joyoadikusumo, and Steven Limcorn. 2025. Lazarus NLP at SemEval-2025 Task 11: Fine-Tuning Large Language Models for Multi-Label Emotion Classification via Sentence-Label Pairing. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 763–772, Vienna, Austria. Association for Computational Linguistics.