MM-IFEngine: Towards Multimodal Instruction Following
Authors: name: Shengyuan Ding, name: Shenxi Wu, name: Xiangyu Zhao, name: Yuhang Zang, name: Haodong Duan, name: Xiaoyi Dong, name: Pan Zhang, name: Yuhang Cao, name: Dahua Lin, name: Jiaqi Wang

The Instruction Following (IF) ability measures how well Multi-modal Large
Language Models (MLLMs) understand exactly what users are telling them and
whether they are doing it right. Existing multimodal instruction following
training data is scarce, the benchmarks are simple with atomic instructions,
and the evaluation strategies are imprecise for tasks demanding exact output
constraints. To address this, we present MM-IFEngine, an effective pipeline to
generate high-quality image-instruction pairs. Our MM-IFEngine pipeline yields
large-scale, diverse, and high-quality training data MM-IFInstruct-23k, which
is suitable for Supervised Fine-Tuning (SFT) and extended as MM-IFDPO-23k for
Direct Preference Optimization (DPO). We further introduce MM-IFEval, a
challenging and diverse multi-modal instruction-following benchmark that
includes (1) both compose-level constraints for output responses and
perception-level constraints tied to the input images, and (2) a comprehensive
evaluation pipeline incorporating both rule-based assessment and judge model.
We conduct SFT and DPO experiments and demonstrate that fine-tuning MLLMs on
MM-IFInstruct-23k and MM-IFDPO-23k achieves notable gains on various IF
benchmarks, such as MM-IFEval (+10.2%), MIA (+7.6%), and IFEval
(+12.3%). The full data and evaluation code will be released on
https://github.com/SYuan03/MM-IFEngine.