Multimodal Vision Language Models (VLMs) have emerged as a transformative technology
at the intersection of computer vision and natural language processing, enabling machines …

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and
languages. As LMMs continue to improve, it is increasingly important to ensure they …

Large-scale deployment of large language models (LLMs) in various applications, such as
chatbots and virtual assistants, requires LLMs to be culturally sensitive to the user to ensure …

Despite recent advancements in vision-language models, their performance remains
suboptimal on images from non-western cultures due to underrepresentation in training …

Vision Language Models (VLMs) often struggle with culture-specific knowledge, particularly
in languages other than English and in underrepresented cultural contexts. To evaluate their …

While numerous recent benchmarks focus on evaluating generic Vision-Language Models
(VLMs), they fall short in addressing the unique demands of geospatial applications. Generic …

Machine Translation of Culture-Specific Items (CSIs) poses significant challenges. Recent
work on CSI translation has shown some success using Large Language Models (LLMs) to …

Warning: this paper contains content that may be offensive or upsetting Hate speech
moderation on global platforms poses unique challenges due to the multimodal and …

Immense effort has been dedicated to minimizing the presence of harmful or biased
generative content and better aligning AI output to human intention; however, research …

Vision-language models (VLMs) have advanced human-AI interaction but struggle with
cultural understanding, often misinterpreting symbols, gestures, and artifacts due to biases …