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    <abstract>We propose a multimodal framework integrating textual context and image caption analysis via systematic data augmentation and parameter-efficient fine-tuning. Our approach features: (1) option shuffling to eliminate positional bias, (2) lexical augmentation through synonym replacement and back-translation, and (3) optimized cross-modal ranking adaptation. The system ranks first in Portuguese (Top-1 Acc: 0.92) and second in English (Top-1 Acc: 0.87) on CodaBench. Experiments across 7B-72B models reveal 32B architectures achieve optimal capacity-trainability balance, while larger 72B models suffer from overfitting. Results demonstrate the limitations of GPT-4 knowledge distillation and emphasize controlled data augmentation for idiomatic language learning, advancing multimodal figurative language processing techniques.</abstract>
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%0 Conference Proceedings
%T CTYUN-AI at SemEval-2025 Task 1: Learning to Rank for Idiomatic Expressions
%A Fan, Yuming
%A Yang, Dongming
%A Cai, Zefeng
%A Lin, Binghuai
%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 fan-etal-2025-ctyun
%X We propose a multimodal framework integrating textual context and image caption analysis via systematic data augmentation and parameter-efficient fine-tuning. Our approach features: (1) option shuffling to eliminate positional bias, (2) lexical augmentation through synonym replacement and back-translation, and (3) optimized cross-modal ranking adaptation. The system ranks first in Portuguese (Top-1 Acc: 0.92) and second in English (Top-1 Acc: 0.87) on CodaBench. Experiments across 7B-72B models reveal 32B architectures achieve optimal capacity-trainability balance, while larger 72B models suffer from overfitting. Results demonstrate the limitations of GPT-4 knowledge distillation and emphasize controlled data augmentation for idiomatic language learning, advancing multimodal figurative language processing techniques.
%U https://aclanthology.org/2025.semeval-1.3/
%P 16-19

Markdown (Informal)

[CTYUN-AI at SemEval-2025 Task 1: Learning to Rank for Idiomatic Expressions](https://aclanthology.org/2025.semeval-1.3/) (Fan et al., SemEval 2025)

• CTYUN-AI at SemEval-2025 Task 1: Learning to Rank for Idiomatic Expressions (Fan et al., SemEval 2025)

ACL

• Yuming Fan, Dongming Yang, Zefeng Cai, and Binghuai Lin. 2025. CTYUN-AI at SemEval-2025 Task 1: Learning to Rank for Idiomatic Expressions. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 16–19, Vienna, Austria. Association for Computational Linguistics.