Use of model-generated content in training causes irreversible loss of distribution tails, termed model collapse, in VAEs, GMMs, and LLMs.
Poisoning and backdooring contrastive learning.arXiv preprint arXiv:2106.09667
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Introduces a text-guided backdoor attack using common textual words as triggers and visual perturbations for stealthy, adjustable control on multimodal pretrained models.
The paper presents a roadmap that identifies four unsolved problems in ML safety: robustness against hazards, monitoring for hazards, alignment of model goals with human intent, and systemic safety.
AI systems lack verifiability, versioning, observability, and traceability in their software supply chains, shown by dependency analysis of 48 projects yielding 4,664 direct and 11,508 transitive dependencies totaling 392M lines of code.
citing papers explorer
-
The Curse of Recursion: Training on Generated Data Makes Models Forget
Use of model-generated content in training causes irreversible loss of distribution tails, termed model collapse, in VAEs, GMMs, and LLMs.
-
Stealthy and Adjustable Text-Guided Backdoor Attacks on Multimodal Pretrained Models
Introduces a text-guided backdoor attack using common textual words as triggers and visual perturbations for stealthy, adjustable control on multimodal pretrained models.
-
Unsolved Problems in ML Safety
The paper presents a roadmap that identifies four unsolved problems in ML safety: robustness against hazards, monitoring for hazards, alignment of model goals with human intent, and systemic safety.
-
The Grand Software Supply Chain of AI Systems
AI systems lack verifiability, versioning, observability, and traceability in their software supply chains, shown by dependency analysis of 48 projects yielding 4,664 direct and 11,508 transitive dependencies totaling 392M lines of code.