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Found 9 papers, 8 papers with code
SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF
As a result, policy optimization is often trapped in a narrow region of the parameter space, leading to suboptimal alignment and performance.
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
Large language models can memorize and repeat their training data, causing privacy and copyright risks.
Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion
As a result, we may be able to craft more potent poisons by carefully choosing the base samples.
What do we learn from inverting CLIP models?
We employ an inversion-based approach to examine CLIP models.
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text
Detecting text generated by modern large language models is thought to be hard, as both LLMs and humans can exhibit a wide range of complex behaviors.
What do Vision Transformers Learn? A Visual Exploration
In addition, we show that ViTs maintain spatial information in all layers except the final layer.
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data back to their rightful owners.
Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise
We observe that the generative behavior of diffusion models is not strongly dependent on the choice of image degradation, and in fact an entire family of generative models can be constructed by varying this choice.
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations
Existing techniques for model inversion typically rely on hard-to-tune regularizers, such as total variation or feature regularization, which must be individually calibrated for each network in order to produce adequate images.
