update

references.bib

@@ -58,7 +58,7 @@ @incollection{helff2024llavaguard
 	title={LLAVAGUARD: VLM-based Safeguard for Vision Dataset Curation and Safety Assessment},
 	author={Lukas Helff and Felix Friedrich and Manuel Brack and Patrick Schramowski and Kristian Kersting},
 	year={2024},
-	booktitle={In Working Notes of the CVPR 2024 Workshop on Responsible Generative AI (ReGenAI)},
+	booktitle={Working Notes of the CVPR 2024 Workshop on Responsible Generative AI (ReGenAI)},
 	url={../../papers/helff2024llavaguard.pdf},
 	Note = {We introduce LlavaGuard, a family of multimodal safeguard models based on Llava, offering a robust framework for evaluating the safety compliance of vision datasets and models. Our models come with a new taxonomy designed for assessing safety risks within visual data. With this safety taxonomy, we have collected and annotated a high-quality dataset to guide Vision-Language Models (VLMs) in safety. We present models in two sizes, namely LlavaGuard-7b and LlavaGuard-13b, both safety-tuned on our novel, annotated dataset to perform policy-based safety assessments of visual content. In this context, LlavaGuard goes beyond binary safety classification by providing information on the violated safety categories, a detailed explanation, and a final assessment. In our evaluations, our models demonstrate state-of-the-art performance with LlavaGuard-13b exhibiting the best results, while the much smaller LlavaGuard-7b model outperforms the much larger Llava-34b baseline. Furthermore, LlavaGuard is designed to allow for customization of the safety taxonomy to align with specific use cases, facilitating zero-shot prompting with individual policies for tailored content moderation},
 Keywords = {AI Safety, Safety Evaluation, Multimodal, Vision Language Model}
@@ -165,7 +165,7 @@ @inproceedings{deiseroth2024dtm
       year={2024},
       Pages = {},
       Keywords = {Quantization, Model Analysdis, Interpretability, Low Compute Setting, Efficiency, Deep Learning},
-      Url={https://arxiv.org/pdf/2312.01544.pdf}
+      Url={https://arxiv.org/pdf/2311.01544}
 }
 
 @inproceedings{kohaut2024icuas,