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    <abstract>Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs’ instruction-following capability primarily focus on verbal instructions in the textual modality. These limitations hinder a thorough analysis of instruction-following capabilities, as they overlook the implicit constraints embedded in the semantically rich visual modality. To address this gap, we introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset that evaluates MLLMs’ instruction-following ability under multimodal settings. Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. Furthermore, by fine-tuning MLLMs on our dataset, we achieve substantial gains in visual instruction-following accuracy and adherence. Through extensive evaluation across representative MLLMs, we provide new insights into the strengths and limitations of current models.</abstract>
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%0 Conference Proceedings
%T Empowering Reliable Visual-Centric Instruction Following in MLLMs
%A He, Weilei
%A Ju, Feng
%A Fan, Zhiyuan
%A Min, Rui
%A Cheng, Minhao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F he-etal-2026-empowering
%X Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user-specified intentions. Nevertheless, existing benchmarks for evaluating MLLMs’ instruction-following capability primarily focus on verbal instructions in the textual modality. These limitations hinder a thorough analysis of instruction-following capabilities, as they overlook the implicit constraints embedded in the semantically rich visual modality. To address this gap, we introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset that evaluates MLLMs’ instruction-following ability under multimodal settings. Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment of how well MLLMs align their outputs with both visual input and textual instructions. Furthermore, by fine-tuning MLLMs on our dataset, we achieve substantial gains in visual instruction-following accuracy and adherence. Through extensive evaluation across representative MLLMs, we provide new insights into the strengths and limitations of current models.
%U https://aclanthology.org/2026.findings-acl.461/
%P 9460-9482

Markdown (Informal)

[Empowering Reliable Visual-Centric Instruction Following in MLLMs](https://aclanthology.org/2026.findings-acl.461/) (He et al., Findings 2026)

• Empowering Reliable Visual-Centric Instruction Following in MLLMs (He et al., Findings 2026)

ACL

• Weilei He, Feng Ju, Zhiyuan Fan, Rui Min, and Minhao Cheng. 2026. Empowering Reliable Visual-Centric Instruction Following in MLLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 9460–9482, San Diego, California, United States. Association for Computational Linguistics.