@inproceedings{wang-etal-2025-enhancing,
title = "Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement",
author = "Wang, Xiyao and
Chen, Jiuhai and
Wang, Zhaoyang and
Zhou, Yuhang and
Zhou, Yiyang and
Yao, Huaxiu and
Zhou, Tianyi and
Goldstein, Tom and
Bhatia, Parminder and
Kass-Hout, Taha and
Huang, Furong and
Xiao, Cao",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.15/",
doi = "10.18653/v1/2025.findings-naacl.15",
pages = "268--282",
ISBN = "979-8-89176-195-7",
abstract = "Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgement, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM{'}s performance and outperforms previous approaches, achieving superior modality alignment."
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<abstract>Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgement, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM’s performance and outperforms previous approaches, achieving superior modality alignment.</abstract>
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%0 Conference Proceedings
%T Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
%A Wang, Xiyao
%A Chen, Jiuhai
%A Wang, Zhaoyang
%A Zhou, Yuhang
%A Zhou, Yiyang
%A Yao, Huaxiu
%A Zhou, Tianyi
%A Goldstein, Tom
%A Bhatia, Parminder
%A Kass-Hout, Taha
%A Huang, Furong
%A Xiao, Cao
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F wang-etal-2025-enhancing
%X Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. However, there remains significant room for improvement in aligning visual and language modalities. Existing methods often depend on external models or data, leading to uncontrollable and unstable alignment results. In this paper, we propose SIMA, a self-improvement framework that enhances visual and language modality alignment without external dependencies. SIMA leverages existing vision instruction tuning datasets to self-generate responses, incorporating an in-context self-critic mechanism that constructs preference pairs for tuning. Crucially, our approach allows LVLMs to act as critics by designing effective critic prompts, eliminating the need for additional fine-tuning with external instruction data. We introduce three novel visual metrics within the self-critic process to guide judgement, significantly improving the accuracy of self-critic. Through extensive experiments across 14 hallucination and comprehensive benchmarks, we demonstrate that SIMA significantly improves LVLM’s performance and outperforms previous approaches, achieving superior modality alignment.
%R 10.18653/v1/2025.findings-naacl.15
%U https://aclanthology.org/2025.findings-naacl.15/
%U https://doi.org/10.18653/v1/2025.findings-naacl.15
%P 268-282
Markdown (Informal)
[Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement](https://aclanthology.org/2025.findings-naacl.15/) (Wang et al., Findings 2025)
ACL
- Xiyao Wang, Jiuhai Chen, Zhaoyang Wang, Yuhang Zhou, Yiyang Zhou, Huaxiu Yao, Tianyi Zhou, Tom Goldstein, Parminder Bhatia, Taha Kass-Hout, Furong Huang, and Cao Xiao. 2025. Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 268–282, Albuquerque, New Mexico. Association for Computational Linguistics.