{"total":56,"rows":[{"id":"2410.22108","title":"Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench","created":"2024-10-29","cited_by_count":64,"area":"mllm"},{"id":"2410.02879","title":"Position: LLM Unlearning Benchmarks are Weak Measures of Progress","created":"2024-10-03","cited_by_count":63,"area":"safety"},{"id":"2410.19482","title":"Measuring memorization in language models via probabilistic extraction","created":"2024-10-25","cited_by_count":41,"area":null},{"id":"2503.05788","title":"Emergent Abilities in Large Language Models: A Survey","created":"2025-02-28","cited_by_count":37,"area":null},{"id":"2410.02650","title":"Undesirable Memorization in Large Language Models: A Survey","created":"2024-10-03","cited_by_count":29,"area":null},{"id":"2503.23084","title":"The Reasoning-Memorization Interplay in Language Models Is Mediated by a Single Direction","created":"2025-03-29","cited_by_count":21,"area":"lr"},{"id":"2510.01645","title":"Position: Privacy Is Not Just Memorization!","created":"2025-10-02","cited_by_count":17,"area":null},{"id":"2505.18091","title":"Data Mixing Can Induce Phase Transitions in Knowledge Acquisition","created":"2025-05-23","cited_by_count":16,"area":null},{"id":"2502.02514","title":"Privacy Attacks on Image AutoRegressive Models","created":"2025-02-04","cited_by_count":15,"area":"diffusion"},{"id":"2410.03249","title":"How Much Can We Forget about Data Contamination?","created":"2024-10-04","cited_by_count":14,"area":null},{"id":"2510.19796","title":"Blackbox Model Provenance via Palimpsestic Membership Inference","created":"2025-10-22","cited_by_count":11,"area":"safety"},{"id":"2502.15680","title":"Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training","created":"2025-02-21","cited_by_count":10,"area":null},{"id":"2507.05578","title":"The Landscape of Memorization in LLMs: Mechanisms, Measurement, and Mitigation","created":"2025-07-08","cited_by_count":10,"area":"safety"},{"id":"2605.29548","title":"Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention","created":"2026-05-28","cited_by_count":10,"area":null},{"id":"2510.18554","title":"Extracting alignment data in open models","created":"2025-10-21","cited_by_count":8,"area":null},{"id":"2412.11044","title":"Understanding and Mitigating Memorization in Diffusion Models for Tabular Data","created":"2024-12-15","cited_by_count":7,"area":"diffusion"},{"id":"2502.05087","title":"Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs","created":"2025-02-07","cited_by_count":7,"area":null},{"id":"2504.09389","title":"Measuring LLM Novelty As The Frontier Of Original And High-Quality Output","created":"2025-04-13","cited_by_count":6,"area":null},{"id":"2504.14452","title":"ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data","created":"2025-04-20","cited_by_count":6,"area":"alignment"},{"id":"2503.01630","title":"Machine Learners Should Acknowledge the Legal Implications of Large Language Models as Personal Data","created":"2025-03-03","cited_by_count":5,"area":null},{"id":"2503.22948","title":"SUV: Scalable Large Language Model Copyright Compliance with Regularized Selective Unlearning","created":"2025-03-29","cited_by_count":5,"area":"alignment"},{"id":"2505.20225","title":"FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models","created":"2025-05-26","cited_by_count":5,"area":"moe"},{"id":"2408.04983","title":"Get Confused Cautiously: Textual Sequence Memorization Erasure with Selective Entropy Maximization","created":"2024-08-09","cited_by_count":4,"area":null},{"id":"2505.22945","title":"OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2506.17265","title":"SUA: Stealthy Multimodal Large Language Model Unlearning Attack","created":"2025-06-10","cited_by_count":4,"area":"mllm"},{"id":"2510.16530","title":"Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks","created":"2025-10-18","cited_by_count":4,"area":null},{"id":"2412.01014","title":"Detecting Memorization in Large Language Models","created":"2024-12-02","cited_by_count":3,"area":null},{"id":"2510.06107","title":"Distributional Semantics Tracing: A Framework for Explaining Hallucinations in Large Language Models","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2511.05518","title":"Retracing the Past: LLMs Emit Training Data When They Get Lost","created":"2025-10-27","cited_by_count":3,"area":null},{"id":"2603.02333","title":"Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects","created":"2026-03-02","cited_by_count":3,"area":"diffusion"},{"id":"2606.06533","title":"Position: Don't Just \"Fix it in Post\": A Science of AI Must Study Training Dynamics","created":"2026-06-03","cited_by_count":3,"area":null},{"id":"2412.18497","title":"Neuron-Level Differentiation of Memorization and Generalization in Large Language Models","created":"2024-12-24","cited_by_count":2,"area":null},{"id":"2502.05159","title":"A Lightweight Method to Disrupt Memorized Sequences in LLM","created":"2025-02-07","cited_by_count":2,"area":null},{"id":"2505.13171","title":"Positional Fragility in LLMs: How Offset Effects Reshape Our Understanding of Memorization Risks","created":"2025-05-19","cited_by_count":2,"area":null},{"id":"2506.18998","title":"Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledge","created":"2025-06-23","cited_by_count":2,"area":null},{"id":"2510.00125","title":"Direct Token Optimization: A Self-contained Approach to Large Language Model Unlearning","created":"2025-09-30","cited_by_count":2,"area":"safety"},{"id":"2510.24256","title":"From Memorization to Reasoning in the Spectrum of Loss Curvature","created":"2025-10-28","cited_by_count":2,"area":"lr"},{"id":"2601.04301","title":"Quantifying the Effect of Test Set Contamination on Generative Evaluations","created":"2026-01-07","cited_by_count":2,"area":null},{"id":"2601.15394","title":"Memorization Dynamics in Knowledge Distillation for Language Models","created":"2026-01-21","cited_by_count":2,"area":"distill"},{"id":"2602.08901","title":"Mitigating Memorization Where It Happens","created":"2026-02-09","cited_by_count":2,"area":null},{"id":"2603.14355","title":"Exposing Long-Tail Safety Failures in Large Language Models through Efficient Diverse Response Sampling","created":"2026-03-15","cited_by_count":2,"area":"alignment"},{"id":"2603.17475","title":"Humans and transformer LMs: Abstraction drives language learning","created":"2026-03-18","cited_by_count":2,"area":null},{"id":"2504.05059","title":"MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2505.22322","title":"A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective","created":"2025-05-28","cited_by_count":1,"area":"diffusion"},{"id":"2507.06056","title":"Data Compressibility Quantifies LLM Memorization","created":"2025-07-08","cited_by_count":1,"area":null},{"id":"2512.10758","title":"Designing AI-Resilient Assessments Using Interconnected Problems: A Theoretically Grounded and Empirically Validated Framework","created":"2025-12-11","cited_by_count":1,"area":null},{"id":"2606.06286","title":"LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs","created":"2026-06-04","cited_by_count":1,"area":null},{"id":"2506.12321","title":"Beyond Frequency: The Role of Redundancy in Large Language Model Memorization","created":"2025-06-14","cited_by_count":0,"area":null},{"id":"2602.03772","title":"UniGeM: Unifying Data Mixing and Selection via Geometric Exploration and Mining","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2605.22981","title":"Memorization Dynamics of Fill-in-the-Middle Pretraining","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2606.18767","title":"Output Vector Editing for Memorization Mitigation in Large Language Models","created":"2026-06-17","cited_by_count":0,"area":null},{"id":"2607.02897","title":"PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement","created":"2026-07-03","cited_by_count":0,"area":"mllm"},{"id":"2609.00184","title":"Synthetic Worlds for Temporal Evaluation and Knowledge Updating in LLMs","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.11029","title":"Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss","created":"2026-09-10","cited_by_count":0,"area":null},{"id":"2609.15039","title":"SpliTEE: Fast and Private LLM Inference by Coupling GPU-Assisted Trusted Execution Environments with Differential Privacy","created":"2026-09-14","cited_by_count":0,"area":null},{"id":"2610.02438","title":"Are you Synthesizing or Recalling? Evaluating LLMs on Algorithmic Code Retrieval","created":"2026-10-01","cited_by_count":0,"area":null}],"next_cursor":null}