@inproceedings{ruan-etal-2025-pai,
title = "{PAI} at {S}em{E}val-2025 Task 11: A Large Language Model Ensemble Strategy for Text-Based Emotion Detection",
author = "Ruan, Zhihao and
You, Runyang and
Yang, Kaifeng and
Lin, Junxin and
Dai, Wenwen and
Zhou, Mengyuan and
Jin, Meizhi and
Mei, Xinyue",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.150/",
pages = "1136--1142",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes our system used in the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. To address the highly subjective nature of emotion detection tasks, we propose a model ensemble strategy designed to capture the varying subjective perceptions of different users towards textual content. The base models of this ensemble strategy consist of several large language models, which are then combined using methods such as neural networks, decision trees, linear regression, and weighted voting. In Track A, out of 28 languages, our system achieved first place in 19 languages. In Track B, out of 11 languages, our system ranked first in 10 languages. Furthermore, our system attained the highest average performance across all languages in both Track A and Track B."
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<abstract>This paper describes our system used in the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. To address the highly subjective nature of emotion detection tasks, we propose a model ensemble strategy designed to capture the varying subjective perceptions of different users towards textual content. The base models of this ensemble strategy consist of several large language models, which are then combined using methods such as neural networks, decision trees, linear regression, and weighted voting. In Track A, out of 28 languages, our system achieved first place in 19 languages. In Track B, out of 11 languages, our system ranked first in 10 languages. Furthermore, our system attained the highest average performance across all languages in both Track A and Track B.</abstract>
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%0 Conference Proceedings
%T PAI at SemEval-2025 Task 11: A Large Language Model Ensemble Strategy for Text-Based Emotion Detection
%A Ruan, Zhihao
%A You, Runyang
%A Yang, Kaifeng
%A Lin, Junxin
%A Dai, Wenwen
%A Zhou, Mengyuan
%A Jin, Meizhi
%A Mei, Xinyue
%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 ruan-etal-2025-pai
%X This paper describes our system used in the SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection. To address the highly subjective nature of emotion detection tasks, we propose a model ensemble strategy designed to capture the varying subjective perceptions of different users towards textual content. The base models of this ensemble strategy consist of several large language models, which are then combined using methods such as neural networks, decision trees, linear regression, and weighted voting. In Track A, out of 28 languages, our system achieved first place in 19 languages. In Track B, out of 11 languages, our system ranked first in 10 languages. Furthermore, our system attained the highest average performance across all languages in both Track A and Track B.
%U https://aclanthology.org/2025.semeval-1.150/
%P 1136-1142
Markdown (Informal)
[PAI at SemEval-2025 Task 11: A Large Language Model Ensemble Strategy for Text-Based Emotion Detection](https://aclanthology.org/2025.semeval-1.150/) (Ruan et al., SemEval 2025)
ACL
- Zhihao Ruan, Runyang You, Kaifeng Yang, Junxin Lin, Wenwen Dai, Mengyuan Zhou, Meizhi Jin, and Xinyue Mei. 2025. PAI at SemEval-2025 Task 11: A Large Language Model Ensemble Strategy for Text-Based Emotion Detection. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 1136–1142, Vienna, Austria. Association for Computational Linguistics.