arXiv Computation and Language
Aug 25

Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning

The paper introduces PM4Bench, a multimodal, multilingual, multi-task benchmark built on a strictly parallel 10‑language corpus, allowing fair cross‑lingual comparison of Large Vision‑Language Models (LVLMs). It also proposes a vision setting that embeds textual inputs directly into images to better mimic real deployment scenarios. Experiments show OCR performance drives cross‑lingual gaps, leading to an OCR‑centric GRPO training strategy that improves multilingual VQA and reduces disparities without costly supervision.

By Junyuan Gao, Jiahe Song, Jiang Wu, Runchuan Zhu, Guanlin Shen, Shasha Wang, Xingjian Wei, Haote Yang, Weijia Li, Bin Wang, Lijun Wu, Conghui He
arXiv AI
Sep 7

Cross-Task Generalization Between Understanding and Generation in Unified Vision-Language Models: A Controlled Study

The study investigates how unified vision‑language models (VLMs) can simultaneously support visual understanding and generation. Using controlled benchmarks (SmartWatch and modified CelebA) that pair VQA, captioning, and text‑to‑image tasks, the authors evaluate several LLM‑based architectures built on SigLIP and VQ‑VAE visual spaces. Results show that mixed training can improve both understanding and generation, but the gains depend on how well the visual input and output spaces are aligned; misaligned or distorted visual spaces can weaken or reverse these benefits. The paper also demonstrates that balancing data across tasks and controlling attribute frequencies can help recover underrepresented visual concepts, and that the transfer is driven more by the base language model’s learned relationships than by visual adapters.

By Jihai Zhang, Tianle Li, Linjie Li, Zhengyuan Yang, Yu Cheng
arXiv Computer Vision
Aug 28

ReViCo: Unveiling the Limitations of VLMs in Visual Text Understanding via Error Correction

ReViCo (Real Visual Correction) is a new benchmark that tests Vision Language Models (VLMs) on the task of correcting text errors in real‑world images, requiring deep understanding of visual text and its context. The study evaluates VLMs using both prompt‑based and targeted training approaches, revealing a significant performance gap between current models and humans. The results show that most VLMs struggle to accurately perceive visual text, leading to frequent correction mistakes, thereby underscoring the need for more robust, text‑aware VLMs.

By Bojun Zhang, Junhong Liang, Feifei Zhai, Fengxian Ji, Yu Zhou