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Found 6 papers, 4 papers with code
User Inference Attacks on Large Language Models
Fine-tuning is a common and effective method for tailoring large language models (LLMs) to specialized tasks and applications.
Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models
Currently, most machine learning models are trained by centralized teams and are rarely updated.
Large Language Models Struggle to Learn Long-Tail Knowledge
The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models.
Music Enhancement via Image Translation and Vocoding
Consumer-grade music recordings such as those captured by mobile devices typically contain distortions in the form of background noise, reverb, and microphone-induced EQ.
Deduplicating Training Data Mitigates Privacy Risks in Language Models
Past work has shown that large language models are susceptible to privacy attacks, where adversaries generate sequences from a trained model and detect which sequences are memorized from the training set.
Universal Adversarial Triggers for Attacking and Analyzing NLP
We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset.
