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Memorization

Papers

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Showing 351–375 of 1088 papers

TitleStatusHype
Learning with Noisy Ground Truth: From 2D Classification to 3D Reconstruction—0
Can LLM Graph Reasoning Generalize beyond Pattern Memorization?Code1
Scaling Laws for Fact Memorization of Large Language Models—0
Protecting Privacy Through Approximating Optimal Parameters for Sequence Unlearning in Language Models—0
Uncovering Latent Memories: Assessing Data Leakage and Memorization Patterns in Frontier AI Models—0
Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning—0
Data Contamination Can Cross Language BarriersCode1
Jogging the Memory of Unlearned LLMs Through Targeted Relearning AttacksCode0
Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in ImagesCode0
Extracting Training Data from Unconditional Diffusion Models—0
How Do Large Language Models Acquire Factual Knowledge During Pretraining?Code1
Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3Code0
Measuring memorization in RLHF for code completion—0
Exposing the Achilles' Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning—0
Automating Pharmacovigilance Evidence Generation: Using Large Language Models to Produce Context-Aware SQL—0
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMsCode2
Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale—0
Exploring Fact Memorization and Style Imitation in LLMs Using QLoRA: An Experimental Study and Quality Assessment Methods—0
Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens—0
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models—0
Self-Tuning: Instructing LLMs to Effectively Acquire New Knowledge through Self-TeachingCode0
Large Language Models Memorize Sensor Datasets! Implications on Human Activity Recognition Research—0
Memorization in deep learning: A survey—0
What Should Embeddings Embed? Autoregressive Models Represent Latent Generating Distributions—0
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping—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