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Found 5 papers, 0 papers with code
Rainbow Teaming: Open-Ended Generation of Diverse Adversarial Prompts
As large language models (LLMs) become increasingly prevalent across many real-world applications, understanding and enhancing their robustness to user inputs is of paramount importance.
Using Captum to Explain Generative Language Models
Captum is a comprehensive library for model explainability in PyTorch, offering a range of methods from the interpretability literature to enhance users' understanding of PyTorch models.
Identifying and Disentangling Spurious Features in Pretrained Image Representations
Neural networks employ spurious correlations in their predictions, resulting in decreased performance when these correlations do not hold.
Tell Your Story: Task-Oriented Dialogs for Interactive Content Creation
We collect a new dataset C3 (Conversational Content Creation), comprising 10k dialogs conditioned on media montages simulated from a large media collection.
Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models
Despite their wide adoption, the underlying training and memorization dynamics of very large language models is not well understood.
