Media
Learning Spatially-Aware Language and Audio Embeddings
Humans can picture a sound scene given an imprecise natural language description. For example, it is easy to imagine an acoustic environment given a phrase like "the
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives Maitreya Patel
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between textual and visual modalities to learn representations.
Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs
To address these issues, we introduced JailTrickBench to evaluate the impact of various attack settings on LLM performance and provide a baseline for jailbreak attacks, encouraging the adoption of a standardized evaluation framework.
Supplementary Materials: FiV A: Fine-grained Visual Attribute Dataset for T ext-to-Image Diffusion Models
Section A. We then introduce additional details on dataset construction in Section B. Further, we Finally, we discuss the limitations and future work of the project in Section D. Please also find the Details on attribute taxonomy and statistics. We visualize the rough distribution of visual attributes and subjects on the left. We also visualize the attribute alignment accuracy via human validation here. Due to space limitations, only 15 sub-subjects are listed for each major-subject. The result shows that Image 4 exhibits inconsistencies, with the reasons provided.
FiV A: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models Tong Wu
Recent advances in text-to-image generation have enabled the creation of high-quality images with diverse applications.
Estimating the Hallucination Rate of Generative AI Andrew Jesson Nicolas Beltran-V elez * Quentin Chu
In ICL, a conditional generative model (CGM) is prompted with a dataset and a prediction question and asked to generate a response.
