@inproceedings{gwak-jung-2026-beyond,
title = "Beyond Single Representations: Multi-Model Embedding Fusion for Stable Text Classification",
author = "Gwak, Jiho and
Jung, Yuchul",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1786/",
doi = "10.18653/v1/2026.acl-long.1786",
pages = "38568--38577",
ISBN = "979-8-89176-390-6",
abstract = "Embedding fusion has become a widely adopted technique for enhancing performance across various NLP tasks. While prior research suggests that different layers of language models encode distinct representations and that pooling strategies influence performance, there is a lack of systematic analysis regarding the empirical efficacy of these differences or the impact of combining embeddings from multiple models. This study provides a rigorous, empirical evaluation of layer-wise fusion strategies to determine their actual contribution to classification performance. Our findings reveal that the effectiveness of individual layers is more dependent on dataset characteristics than on the model architecture itself. Furthermore, we demonstrate that fusing embeddings from multiple models yields more robust and consistent representations across tasks, with the influence of any single model diminishing as the number of integrated models increases. Notably, experiments on low-resource datasets show that embedding fusion provides particularly significant gains when training data is scarce, highlighting its robustness and adaptability in data-constrained environments. We also analyze the trade-off between performance gains and computational overhead, and discuss which fusion configurations provide the best balance between stability and efficiency."
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%0 Conference Proceedings
%T Beyond Single Representations: Multi-Model Embedding Fusion for Stable Text Classification
%A Gwak, Jiho
%A Jung, Yuchul
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F gwak-jung-2026-beyond
%X Embedding fusion has become a widely adopted technique for enhancing performance across various NLP tasks. While prior research suggests that different layers of language models encode distinct representations and that pooling strategies influence performance, there is a lack of systematic analysis regarding the empirical efficacy of these differences or the impact of combining embeddings from multiple models. This study provides a rigorous, empirical evaluation of layer-wise fusion strategies to determine their actual contribution to classification performance. Our findings reveal that the effectiveness of individual layers is more dependent on dataset characteristics than on the model architecture itself. Furthermore, we demonstrate that fusing embeddings from multiple models yields more robust and consistent representations across tasks, with the influence of any single model diminishing as the number of integrated models increases. Notably, experiments on low-resource datasets show that embedding fusion provides particularly significant gains when training data is scarce, highlighting its robustness and adaptability in data-constrained environments. We also analyze the trade-off between performance gains and computational overhead, and discuss which fusion configurations provide the best balance between stability and efficiency.
%R 10.18653/v1/2026.acl-long.1786
%U https://aclanthology.org/2026.acl-long.1786/
%U https://doi.org/10.18653/v1/2026.acl-long.1786
%P 38568-38577
Markdown (Informal)
[Beyond Single Representations: Multi-Model Embedding Fusion for Stable Text Classification](https://aclanthology.org/2026.acl-long.1786/) (Gwak & Jung, ACL 2026)
ACL