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Memorization

Papers

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Showing 551–600 of 1088 papers

TitleStatusHype
Thinking Tokens for Language Modeling—0
Beyond Scaling Laws: Understanding Transformer Performance with Associative Memory—0
An Inversion-based Measure of Memorization for Diffusion ModelsCode0
To Each (Textual Sequence) Its Own: Improving Memorized-Data Unlearning in Large Language Models—0
Exploring prompts to elicit memorization in masked language model-based named entity recognition—0
Mothman at SemEval-2024 Task 9: An Iterative System for Chain-of-Thought Prompt Optimization—0
Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics—0
Post-hoc and manifold explanations analysis of facial expression data based on deep learningCode0
Quantifying Memorization and Detecting Training Data of Pre-trained Language Models using Japanese Newspaper—0
Rethinking LLM Memorization through the Lens of Adversarial Compression—0
Does Your Neural Code Completion Model Use My Code? A Membership Inference Approach—0
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion—0
The Positivity of the Neural Tangent Kernel—0
The Fault in our Stars: Quality Assessment of Code Generation Benchmarks—0
AI Knowledge and Reasoning: Emulating Expert Creativity in Scientific Research—0
GP-MoLFormer: A Foundation Model For Molecular Generation—0
Towards Better Generalization in Open-Domain Question Answering by Mitigating Context Memorization—0
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks—0
SoK: A Review of Differentially Private Linear Models For High-Dimensional Data—0
Towards Memorization-Free Diffusion Models—0
Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNsCode0
Protecting Copyrighted Material with Unique Identifiers in Large Language Model TrainingCode0
DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-TuningCode0
Tackling Noisy Labels with Network Parameter Additive DecompositionCode0
Self-generated Replay Memories for Continual Neural Machine TranslationCode0
Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain AdaptationCode0
Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement—0
Ethos: Rectifying Language Models in Orthogonal Parameter Space—0
Alpaca against Vicuna: Using LLMs to Uncover Memorization of LLMsCode0
LLM-Oriented Retrieval Tuner—0
ROME: Memorization Insights from Text, Logits and Representation—0
Unveiling Privacy, Memorization, and Input Curvature Links—0
Learning Associative Memories with Gradient Descent—0
Unified View of Grokking, Double Descent and Emergent Abilities: A Perspective from Circuits Competition—0
Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language ModelsCode0
Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?Code0
Amplifying Training Data Exposure through Fine-Tuning with Pseudo-Labeled MembershipsCode0
Towards Uncovering How Large Language Model Works: An Explainability Perspective—0
Neural Information Organizing and Processing -- Neural Machines—0
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization—0
Social Evolution of Published Text and The Emergence of Artificial Intelligence Through Large Language Models and The Problem of Toxicity and Bias—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
Wasserstein proximal operators describe score-based generative models and resolve memorization—0
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models—0
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels—0
Analyzing the Neural Tangent Kernel of Periodically Activated Coordinate Networks—0
EMN: Brain-inspired Elastic Memory Network for Quick Domain Adaptive Feature Mapping—0
Human-Centered Privacy Research in the Age of Large Language Models—0
Déjà Vu Memorization in Vision-Language Models—0
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic DataCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PaLM-540B (few-shot, k=5)Accuracy95.4—Unverified
2Gopher-280B (few-shot, k=5)Accuracy80—Unverified
3PaLM-62B (few-shot, k=5)Accuracy77.7—Unverified