Memorization in language models increases log-linearly with model capacity, data duplication count, and prompt context length.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
unclear 1representative citing papers
Framework quantifies intra- and inter-client memorization in FL LLMs, finding higher intra-client memorization influenced by decoding strategies, prefix length, and FL algorithms.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
citing papers explorer
-
Quantifying Memorization Across Neural Language Models
Memorization in language models increases log-linearly with model capacity, data duplication count, and prompt context length.
-
Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning
Framework quantifies intra- and inter-client memorization in FL LLMs, finding higher intra-client memorization influenced by decoding strategies, prefix length, and FL algorithms.
-
Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.