World PulseNow
Newsletter

OutSafe-Bench: A Benchmark for Multimodal Offensive Content Detection in Large Language Models

arXiv — cs.LG•Friday, November 14, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of OutSafe-Bench marks a significant advancement in the evaluation of multimodal large language models (MLLMs), addressing the urgent need for comprehensive safety benchmarks. Current benchmarks, as highlighted in related works like ShortV and CHOICE, often fall short in evaluating the full spectrum of model capabilities and risks. OutSafe-Bench's extensive dataset, featuring over 18,000 bilingual prompts and various media types, is crucial for assessing the safety of MLLMs. This aligns with the findings from CHOICE, which emphasizes the importance of robust evaluation frameworks in understanding model performance in diverse contexts.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
HiEAG: Evidence-Augmented Generation for Out-of-Context Misinformation Detection
arXiv — cs.CL12 hours ago
HiEAG: Evidence-Augmented Generation for Out-of-Context Misinformation Detection
PositiveArtificial Intelligence
Recent advancements in out-of-context (OOC) misinformation detection have highlighted the need for improved consistency checks between image-text pairs and external evidence. The proposed HiEAG framework aims to enhance this process by utilizing multimodal large language models (MLLMs) to refine external consistency checking. This approach includes a comprehensive pipeline that integrates evidence reranking and rewriting, addressing the limitations of current methods that focus primarily on internal consistency.
Read full article
via arXiv — cs.CL
Unifying Segment Anything in Microscopy with Vision-Language Knowledge
arXiv — cs.CV3 days ago
Unifying Segment Anything in Microscopy with Vision-Language Knowledge
PositiveArtificial Intelligence
The paper titled 'Unifying Segment Anything in Microscopy with Vision-Language Knowledge' discusses the importance of accurate segmentation in biomedical images. It highlights the limitations of existing models in handling unseen domain data due to a lack of vision-language knowledge. The authors propose a new framework, uLLSAM, which utilizes Multimodal Large Language Models (MLLMs) to enhance segmentation performance. This approach aims to improve generalization capabilities across cross-domain datasets, achieving notable performance improvements.
Read full article
via arXiv — cs.CV