A cross-modal alignment attack achieves AUC 0.821 for single-sample black-box membership inference on VLMs such as LLaVA-1.5 by quantifying image-generated caption similarity.
Yi: Open foundation models by 01.ai, 2024
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Learned Task Vectors trained directly outperform extracted task vectors for in-context learning with added mechanistic insights into linear propagation and key attention circuits.
A new framework using Task Subspace Logit Attribution localizes attention heads specialized for task recognition and task learning in in-context learning, showing they align and rotate hidden states within a task subspace.
Fine-tuned MedGemma outperforms untuned GPT-4 in zero-shot medical image disease classification, achieving 80.37% versus 69.58% mean test accuracy with higher sensitivity for cancer and pneumonia.
citing papers explorer
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Single-Sample Black-Box Membership Inference Attack against Vision-Language Models via Cross-modal Semantic Alignment
A cross-modal alignment attack achieves AUC 0.821 for single-sample black-box membership inference on VLMs such as LLaVA-1.5 by quantifying image-generated caption similarity.
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Task Vectors, Learned Not Extracted: Performance Gains and Mechanistic Insight
Learned Task Vectors trained directly outperform extracted task vectors for in-context learning with added mechanistic insights into linear propagation and key attention circuits.
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Localizing Task Recognition and Task Learning in In-Context Learning via Attention Head Analysis
A new framework using Task Subspace Logit Attribution localizes attention heads specialized for task recognition and task learning in in-context learning, showing they align and rotate hidden states within a task subspace.
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MedGemma vs GPT-4: Open-Source and Proprietary Zero-shot Medical Disease Classification from Images
Fine-tuned MedGemma outperforms untuned GPT-4 in zero-shot medical image disease classification, achieving 80.37% versus 69.58% mean test accuracy with higher sensitivity for cancer and pneumonia.