| Challenge: | VLMs (Vision-Language Models) can be induced to generate harmful or inaccurate content through specific test cases. |
| Approach: | They propose a red teaming dataset which encompasses 12 subtasks under 4 primary aspects (faithfulness, privacy, safety, fairness) this dataset is the first to benchmark current VLMs in terms of these 4 aspects . |
| Outcome: | The proposed dataset shows that 10 open-source VLMs struggle with red teaming in different degrees and have up to 31% performance gap with GPT-4V. |
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| Challenge: | Existing approaches focus on improving attack success rates while overlooking the need for comprehensive test case coverage. |
| Approach: | They propose a top-down approach to automated red teaming that scales up the diversity of test cases using an extensible, fine-grained risk taxonomy. |
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Toxicity Red-Teaming: Benchmarking LLM Safety in Singapore’s Low-Resource Languages (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have transformed natural language processing, but their safety mechanisms remain under-explored in low-resource, multilingual settings. |
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Re-Align: Aligning Vision Language Models via Retrieval-Augmented Direct Preference Optimization (2025.emnlp-main)
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Shuo Xing, Peiran Li, Yuping Wang, Ruizheng Bai, Yueqi Wang, Chan-Wei Hu, Chengxuan Qian, Huaxiu Yao, Zhengzhong Tu
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Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)
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| Challenge: | Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers. |
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| Challenge: | Recent studies have focused on the compositionality of vision-language models (VLMs) however, the performance of GVLMs in multimodal compositional reasoning remains under-explored. |
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| Challenge: | Large Language Models (LLMs) excel in natural language processing tasks but are vulnerable to harmful content and being exploited for malicious purposes. |
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Multitask-Bench: Unveiling and Mitigating Safety Gaps in LLMs Fine-tuning (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to their adoption across a wide range of tasks, ranging from code generation to machine translation and sentiment analysis. |
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Code-Switching Red-Teaming: LLM Evaluation for Safety and Multilingual Understanding (2025.acl-long)
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| Challenge: | Recent large language models (LLMs) are inherently multilingual agents . concerns regarding their safety have emerged . |
| Approach: | They propose a framework to synthesize red-teaming queries and investigate their safety . they demonstrate that the framework outperforms existing red- teaming techniques . |
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