SOTAVerified

Memorization

Papers

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Showing 676–700 of 1088 papers

TitleStatusHype
Wasserstein proximal operators describe score-based generative models and resolve memorization—0
What Can Transformer Learn with Varying Depth? Case Studies on Sequence Learning Tasks—0
What can we learn from Data Leakage and Unlearning for Law?—0
What do larger image classifiers memorise?—0
What Do Neural Networks Learn When Trained With Random Labels?—0
What is Adversarial Training for Diffusion Models?—0
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions—0
What Should LLMs Forget? Quantifying Personal Data in LLMs for Right-to-Be-Forgotten Requests—0
When Can Memorization Improve Fairness?—0
When do you need Chain-of-Thought Prompting for ChatGPT?—0
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale—0
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?—0
When Reasoning Meets Compression: Benchmarking Compressed Large Reasoning Models on Complex Reasoning Tasks—0
Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test—0
Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes—0
White-box Membership Attack Against Machine Learning Based Retinopathy Classification—0
Why are state-space models more expressive than n-gram models?—0
Towards Understanding Clean Generalization and Robust Overfitting in Adversarial Training—0
Why Diffusion Models Don't Memorize: The Role of Implicit Dynamical Regularization in Training—0
Why Does ChatGPT Fall Short in Providing Truthful Answers?—0
Why Train More? Effective and Efficient Membership Inference via Memorization—0
Wide and Deep Learning for Peer-to-Peer Lending—0
WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs—0
WinoWhat: A Parallel Corpus of Paraphrased WinoGrande Sentences with Common Sense Categorization—0
Winter Soldier: Backdooring Language Models at Pre-Training with Indirect Data Poisoning—0
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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