Cover Image

Vision Language Models Are Not (Yet) Spelling Correctors

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liang, Junhong, Zhang, Bojun
Format:	Preprint
Published:	2025
Subjects:	Computation and Language Computer Vision and Pattern Recognition
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models
by: Jian, Pu, et al.
Published: (2025)

Dynamic Token Reweighting for Robust Vision-Language Models
by: Jiang, Tanqiu, et al.
Published: (2025)

HEED: Density-Weighted Residual Alignment for Hybrid Vision-Language Model Distillation
by: Liang, Yihao, et al.
Published: (2026)

A Picture is Worth a Thousand (Correct) Captions: A Vision-Guided Judge-Corrector System for Multimodal Machine Translation
by: Betala, Siddharth, et al.
Published: (2025)

Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance
by: Zhao, Haozhe, et al.
Published: (2024)

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model
by: Zang, Yuhang, et al.
Published: (2025)

Light Up the Shadows: Enhance Long-Tailed Entity Grounding with Concept-Guided Vision-Language Models
by: Zhang, Yikai, et al.
Published: (2024)

Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models
by: Wang, Weihang, et al.
Published: (2025)

How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?
by: Lee, Seongyun, et al.
Published: (2024)

Scalable Vision Language Model Training via High Quality Data Curation
by: Dong, Hongyuan, et al.
Published: (2025)

Can We Predict Performance of Large Models across Vision-Language Tasks?
by: Zhao, Qinyu, et al.
Published: (2024)

Are Bigger Encoders Always Better in Vision Large Models?
by: Li, Bozhou, et al.
Published: (2024)

LoMo: Local Modality Substitution for Deeper Vision-Language Fusion
by: Han, Feng, et al.
Published: (2026)

MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly
by: Wang, Zhaowei, et al.
Published: (2025)

Dream-VL & Dream-VLA: Open Vision-Language and Vision-Language-Action Models with Diffusion Language Model Backbone
by: Ye, Jiacheng, et al.
Published: (2025)

Can Large Vision-Language Models Understand Multimodal Sarcasm?
by: Wang, Xinyu, et al.
Published: (2025)

Lost in Embeddings: Information Loss in Vision-Language Models
by: Li, Wenyan, et al.
Published: (2025)

A Unified Hallucination Mitigation Framework for Large Vision-Language Models
by: Chang, Yue, et al.
Published: (2024)

CLIP-Adapter: Better Vision-Language Models with Feature Adapters
by: Gao, Peng, et al.
Published: (2021)

PROGRESSLM: Towards Progress Reasoning in Vision-Language Models
by: Zhang, Jianshu, et al.
Published: (2026)

SynthVLM: Towards High-Quality and Efficient Synthesis of Image-Caption Datasets for Vision-Language Models
by: Liu, Zheng, et al.
Published: (2024)

Multimodal Abstractive Summarization of Instructional Videos with Vision-Language Models
by: Nazir, Maham, et al.
Published: (2026)

Beyond the Vision Encoder: Identifying and Mitigating Spatial Bias in Large Vision-Language Models
by: Zhu, Yingjie, et al.
Published: (2025)

Inference Compute-Optimal Video Vision Language Models
by: Wang, Peiqi, et al.
Published: (2025)

Conflict Adaptation in Vision-Language Models
by: Hu, Xiaoyang
Published: (2025)

Vision Language Models are Confused Tourists
by: Irawan, Patrick Amadeus, et al.
Published: (2025)

NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization
by: Zhang, Zheyuan, et al.
Published: (2025)

FlightGPT: Towards Generalizable and Interpretable UAV Vision-and-Language Navigation with Vision-Language Models
by: Cai, Hengxing, et al.
Published: (2025)

Adaptive Vision-Language Model Routing for Computer Use Agents
by: Liu, Xunzhuo, et al.
Published: (2026)

Mitigating Hallucinations in Large Vision-Language Models by Self-Injecting Hallucinations
by: Lu, Yifan, et al.
Published: (2025)

The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
by: Zhao, Qinyu, et al.
Published: (2024)

Expanding the Boundaries of Vision Prior Knowledge in Multi-modal Large Language Models
by: Liang, Qiao, et al.
Published: (2025)

VScan: Rethinking Visual Token Reduction for Efficient Large Vision-Language Models
by: Zhang, Ce, et al.
Published: (2025)

PhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining
by: Liang, Cheng, et al.
Published: (2026)

Evaluation and Enhancement of Semantic Grounding in Large Vision-Language Models
by: Lu, Jiaying, et al.
Published: (2023)

NPHardEval4V: Dynamic Evaluation of Large Vision-Language Models with Effects of Vision
by: Li, Xiang, et al.
Published: (2024)

Natural Language Inference Improves Compositionality in Vision-Language Models
by: Cascante-Bonilla, Paola, et al.
Published: (2024)

Do Vision-Language Models Really Understand Visual Language?
by: Hou, Yifan, et al.
Published: (2024)

Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions
by: Jian, Pu, et al.
Published: (2025)

Vision-Language Models Do Not Understand Negation
by: Alhamoud, Kumail, et al.
Published: (2025)