Memorization in Generative Models

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8 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
Apr 23, 2026Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4LLM Evaluation Memorization in Language Models
Nara Institute of Science and Technology · Future Corporation
EngramaBench: Evaluating Long-Term Conversational Memory with Structured Graph Retrieval
Apr 23, 2026Julian AcunaLLM Evaluation Memorization in Language Models
SCM: Sleep-Consolidated Memory with Algorithmic Forgetting for Large Language Models
Apr 22, 2026Saish Sachin ShindeMemory-Augmented Language Models Memorization in Language Models
Clyrai IP Studio
AnchorMem: Anchored Facts with Associative Contexts for Building Memory in Large Language Models
Apr 19, 2026Zhanyu Shen, Sijie Cheng, Zhicheng Guo +3Memory-Augmented Language Models Memorization in Language Models
College of Computer Science and Software Engineering, Shenzhen University · Tsinghua University
Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Language Models
Mar 21, 2026Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg +1LLM Alignment Memorization in Language Models
Stony Brook University · Carnegie Mellon University · Columbia Law School
Generalization and Memorization in Rectified Flow
Mar 12, 2026Mingxing Rao, Daniel MoyerRectified Flow Membership Inference Attacks
Vanderbilt University, Nashville TN 37235, USA
Quantifying the Effect of Test Set Contamination on Generative Evaluations
Jan 7, 2026Rylan Schaeffer, Joshua Kazdan, Baber Abbasi +8Language Model Generation Evaluation Benchmark Contamination
Stanford Computer Science · Stanford Statistics · EleutherAI +3
Continual Memorization of Factoids in Language Models
Nov 11, 2024Howard Chen, Jiayi Geng, Adithya Bhaskar +2Continual Learning for LLMs Memorization in Language Models
Princeton Language and Intelligence (PLI), Princeton University
Mitigating Memorization In Language Models
Oct 3, 2024Mansi Sakarvadia, Aswathy Ajith, Arham Khan +6Memorization in Language Models LLM Fine-Tuning
University of Chicago · Argonne National Laboratory · Lawrence Berkeley National Laboratory +3
Smoothing the Score Function to Enhance Generalization in Diffusion Models
Date pendingXinyu Zhou, Jiawei Zhang, Stephen J. WrightMemorization in Generative Models Generative Modeling
Department of Computer Sciences, University of Wisconsin Madison, WI, USA.

Apr 23, 2026, cs.CLJ/K move · Enter open · S save

Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms

Yuto Nishida, Naoki Shikoda, Yosuke Kishinami, Ryo Fujii+3

Nara Institute of Science and Technology · Future Corporation

LLM Evaluation Memorization in Language Models Neural Network Memorization Memorization in Generative Models Factual Knowledge in Language Models Language Model Robustness+1

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Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entity-based QA is a common framework for analyzing non-verbatim memorization, but typical evaluations query each entity using a single canonical surface form, making it difficult to disentangle fact memorization from access through a particular name. We introduce RedirectQA, an entity-based QA dataset that uses Wikipedia redirect information to associate Wikidata factual triples with categorized surface forms for each entity, including alternative names, abbreviations, spelling variants, and common erroneous forms. Across 13 LLMs, we examine surface-conditioned factual memorization and find that prediction outcomes often change when only the entity surface form changes. This inconsistency is category-dependent: models are more robust to minor orthographic variations than to larger lexical variations such as aliases and abbreviations. Frequency analyses further suggest that both entity- and surface-level frequencies are associated with accuracy, and that entity frequency often contributes beyond surface frequency. Overall, factual memorization appears neither purely surface-specific nor fully surface-invariant, highlighting the importance of surface-form diversity in evaluating non-verbatim memorization.

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