DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News

Tengxiao Lv; Juntao Li; Chao Liu; Yiyang Kang; Ling Luo (罗凌); Yuanyuan Sun (孙媛媛, 孙嫒媛); Hongfei Lin (林鸿飞)

DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News

Tengxiao Lv, Juntao Li, Chao Liu, Yiyang Kang, Ling Luo, Yuanyuan Sun, Hongfei Lin

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Abstract

We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).

Anthology ID:: 2025.semeval-1.25
Volume:: Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
Month:: July
Year:: 2025
Address:: Vienna, Austria
Editors:: Sara Rosenthal, Aiala Rosá, Debanjan Ghosh, Marcos Zampieri
Venues:: SemEval | WS
SIG:
Publisher:: Association for Computational Linguistics
Note:
Pages:: 174–179
Language:
URL:: https://aclanthology.org/2025.semeval-1.25/
DOI:
Bibkey:
Cite (ACL):: Tengxiao Lv, Juntao Li, Chao Liu, Yiyang Kang, Ling Luo, Yuanyuan Sun, and Hongfei Lin. 2025. DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 174–179, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):: DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News (Lv et al., SemEval 2025)
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PDF:: https://aclanthology.org/2025.semeval-1.25.pdf

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@inproceedings{lv-etal-2025-dutir,
    title = "{DUTIR} at {S}em{E}val-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News",
    author = "Lv, Tengxiao  and
      Li, Juntao  and
      Liu, Chao  and
      Kang, Yiyang  and
      Luo, Ling  and
      Sun, Yuanyuan  and
      Lin, Hongfei",
    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-1.25/",
    pages = "174--179",
    ISBN = "979-8-89176-273-2",
    abstract = "We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model{'}s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian)."
}

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%0 Conference Proceedings
%T DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News
%A Lv, Tengxiao
%A Li, Juntao
%A Liu, Chao
%A Kang, Yiyang
%A Luo, Ling
%A Sun, Yuanyuan
%A Lin, Hongfei
%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 lv-etal-2025-dutir
%X We propose a multilingual text processing framework that combines multilingual translation with data augmentation, QLoRA-based multi-model fine-tuning, and GLM-4-Plus-based ensemble classification. By using GLM-4-Plus to translate multilingual texts into English, we enhance data diversity and quantity. Data augmentation effectively improves the model’s performance on imbalanced datasets. QLoRA fine-tuning optimizes the model and reduces classification loss. GLM-4-Plus, as a meta-classifier, further enhances system performance. Our system achieved first place in three languages (English, Portuguese and Russian).
%U https://aclanthology.org/2025.semeval-1.25/
%P 174-179

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Markdown (Informal)

[DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News](https://aclanthology.org/2025.semeval-1.25/) (Lv et al., SemEval 2025)

DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News (Lv et al., SemEval 2025)

ACL

Tengxiao Lv, Juntao Li, Chao Liu, Yiyang Kang, Ling Luo, Yuanyuan Sun, and Hongfei Lin. 2025. DUTIR at SemEval-2025 Task 10: A Large Language Model-based Approach for Entity Framing in Online News. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 174–179, Vienna, Austria. Association for Computational Linguistics.