arXiv:2606.04588v2 Announce Type: replace
Abstract: Multimodal large language models have made rapid progress in video understanding, yet existing benchmarks largely rely on simple prompts and provid...
By Huangchen Xu, Yuan Wu, Yi Chang
Video-IFBench is a new benchmark designed to evaluate how well multimodal large language models (MLLMs) follow user-specified instructions in video understanding tasks. It introduces an instruction taxonomy with four templates—single-task, multi-task, selection, and nested—covering 32 task types and 39 constraint categories that span semantic and format requirements. The benchmark was built using a semi-automatic pipeline that combines MLLMs, programmatic processing, and human verification, producing 1.5K samples, and a large-scale evaluation of over 20 recent MLLMs shows that instruction following remains difficult, especially for complex constraints and conditional structures.
By Hongbo Liu, Peixian Chen, Sihan Liu, Peiyuan Zhang, Kai Zou, Dian Zheng, Xiaoxing Hu, Yuhao Dong, Mengdan Zhang, Yunhang Shen, Haoyu Cao, Wei Liu, Weibo Gu, Xing Sun, Shengjie Zhao
arXiv:2608. 19207v1 Announce Type: new Abstract: Production deployments of Multimodal Large Language Models (MLLMs) increasingly rely on system messages to govern model behavior.
By Juan Yeo, Geewook Kim
arXiv:2503. 06573v3 Announce Type: replace-cross Abstract: Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge.
By Gili Lior, Asaf Yehudai, Ariel Gera, Liat Ein-Dor
Instruction-following ability is critical for deploying large language models in real-world applications, where downstream components depend on the output satisfying specific constraints. Modern deployments increasingly handle the full task in a single LLM call, with one prompt specifying a layered output whose overall artifact, structural sections, and nested fields must each satisfy concrete constraints.
MM-IFEval-Pro is a new multilingual benchmark for evaluating instruction-following in vision-language models, covering both Chinese and English tasks. It includes 4 major task categories, 24 subcategories, and 8 instruction categories with 52 subcategories, each sample featuring an average of 3.0 constraints to mimic complex instruction scenarios. A reinforcement-learning training set with Chinese and adversarial instructions improves model performance on MM-IFEval-Pro and transfers well to other multimodal benchmarks, showing strong cross-task and cross-language generalization.
By Changming Xiao, Zhenliang Ni, Jinhui He, Han Shu, Jie Hu