Multimodal Cultural Safety: Evaluation Framework and Alignment Strategies

arXiv — cs.CLTuesday, December 23, 2025 at 5:00:00 AM
  • A new evaluation framework called CROSS has been introduced to assess the cultural safety reasoning capabilities of large vision-language models (LVLMs), addressing the gap in existing benchmarks that primarily focus on physical safety. CROSS includes 1,284 multilingual queries from 16 countries, emphasizing the importance of cultural context in interpreting visual data.
  • This development is significant as it aims to enhance the deployment of LVLMs in globally distributed applications, such as tourism assistants, ensuring that responses are culturally appropriate and sensitive to diverse norms.
  • The introduction of CROSS highlights a growing recognition of the need for cultural awareness in AI systems, paralleling ongoing discussions about the vulnerabilities of multimodal models to harmful prompts and the necessity for frameworks that ensure responsible AI governance across different cultural contexts.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps
Continue Readings
ClimateIQA: A New Dataset and Benchmark to Advance Vision-Language Models in Meteorology Anomalies Analysis
PositiveArtificial Intelligence
A new dataset named ClimateIQA has been introduced to enhance the capabilities of Vision-Language Models (VLMs) in analyzing meteorological anomalies. This dataset, which includes 26,280 high-quality images, aims to address the challenges faced by existing models like GPT-4o and Qwen-VL in interpreting complex meteorological heatmaps characterized by irregular shapes and color variations.
LLaVAction: evaluating and training multi-modal large language models for action understanding
PositiveArtificial Intelligence
The research titled 'LLaVAction' focuses on evaluating and training multi-modal large language models (MLLMs) for action understanding, reformulating the EPIC-KITCHENS-100 dataset into a benchmark for MLLMs. The study reveals that leading MLLMs struggle with recognizing correct actions when faced with difficult distractors, highlighting a gap in their fine-grained action understanding capabilities.
DriveRX: A Vision-Language Reasoning Model for Cross-Task Autonomous Driving
PositiveArtificial Intelligence
DriveRX has been introduced as a vision-language reasoning model aimed at enhancing cross-task autonomous driving by addressing the limitations of traditional end-to-end models, which struggle with complex scenarios due to a lack of structured reasoning. This model is part of a broader framework called AutoDriveRL, which optimizes four core tasks through a unified training approach.

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about