@inproceedings{wang-etal-2024-m2pt,
title = "{M}$^2${PT}: Multimodal Prompt Tuning for Zero-shot Instruction Learning",
author = "Wang, Taowen and
Liu, Yiyang and
Liang, James Chenhao and
Zhao, Junhan and
Cui, Yiming and
Mao, Yuning and
Nie, Shaoliang and
Liu, Jiahao and
Feng, Fuli and
Xu, Zenglin and
Han, Cheng and
Huang, Lifu and
Wang, Qifan and
Liu, Dongfang",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.218",
doi = "10.18653/v1/2024.emnlp-main.218",
pages = "3723--3740",
abstract = "Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization capabilities for unseen tasks across various modalities. Instruction tuning has emerged as an effective strategy for achieving zero-shot generalization by finetuning pretrained models on diverse multimodal tasks. As the scale of MLLMs continues to grow, parameter-efficient finetuning becomes increasingly critical. However, most existing parameter-efficient approaches focus only on single modalities and often overlook the multimodal characteristics during finetuning. In this work, we introduce a novel Multimodal Prompt Tuning (M$^2$PT) approach for efficient instruction tuning of MLLMs. M$^2$PT effectively integrates visual and textual prompts into the vision encoder and language processor respectively during finetuning, facilitating the extraction and alignment of features across modalities. Empirical results on various multimodal evaluation datasets demonstrate the superior performance of our approach compared to several state-of-the-art baselines. A comprehensive set of ablation studies validates the effectiveness of our prompt design and the efficiency of our approach.",
}
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<abstract>Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization capabilities for unseen tasks across various modalities. Instruction tuning has emerged as an effective strategy for achieving zero-shot generalization by finetuning pretrained models on diverse multimodal tasks. As the scale of MLLMs continues to grow, parameter-efficient finetuning becomes increasingly critical. However, most existing parameter-efficient approaches focus only on single modalities and often overlook the multimodal characteristics during finetuning. In this work, we introduce a novel Multimodal Prompt Tuning (M²PT) approach for efficient instruction tuning of MLLMs. M²PT effectively integrates visual and textual prompts into the vision encoder and language processor respectively during finetuning, facilitating the extraction and alignment of features across modalities. Empirical results on various multimodal evaluation datasets demonstrate the superior performance of our approach compared to several state-of-the-art baselines. A comprehensive set of ablation studies validates the effectiveness of our prompt design and the efficiency of our approach.</abstract>
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%0 Conference Proceedings
%T M²PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
%A Wang, Taowen
%A Liu, Yiyang
%A Liang, James Chenhao
%A Zhao, Junhan
%A Cui, Yiming
%A Mao, Yuning
%A Nie, Shaoliang
%A Liu, Jiahao
%A Feng, Fuli
%A Xu, Zenglin
%A Han, Cheng
%A Huang, Lifu
%A Wang, Qifan
%A Liu, Dongfang
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F wang-etal-2024-m2pt
%X Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization capabilities for unseen tasks across various modalities. Instruction tuning has emerged as an effective strategy for achieving zero-shot generalization by finetuning pretrained models on diverse multimodal tasks. As the scale of MLLMs continues to grow, parameter-efficient finetuning becomes increasingly critical. However, most existing parameter-efficient approaches focus only on single modalities and often overlook the multimodal characteristics during finetuning. In this work, we introduce a novel Multimodal Prompt Tuning (M²PT) approach for efficient instruction tuning of MLLMs. M²PT effectively integrates visual and textual prompts into the vision encoder and language processor respectively during finetuning, facilitating the extraction and alignment of features across modalities. Empirical results on various multimodal evaluation datasets demonstrate the superior performance of our approach compared to several state-of-the-art baselines. A comprehensive set of ablation studies validates the effectiveness of our prompt design and the efficiency of our approach.
%R 10.18653/v1/2024.emnlp-main.218
%U https://aclanthology.org/2024.emnlp-main.218
%U https://doi.org/10.18653/v1/2024.emnlp-main.218
%P 3723-3740
Markdown (Informal)
[M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning](https://aclanthology.org/2024.emnlp-main.218) (Wang et al., EMNLP 2024)
ACL
- Taowen Wang, Yiyang Liu, James Chenhao Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, and Dongfang Liu. 2024. M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 3723–3740, Miami, Florida, USA. Association for Computational Linguistics.