ICML 2025 Past Large language modelsSafety & alignment
The Impact of Memorization on Trustworthy Foundation Models: ICML 2025 Workshop
MemFM
- Submission deadline
- May 28, 2025, 11:59 UTC imported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Auto-imported from the OpenReview venue record on 2026-06-10 — please verify and enrich (topics are keyword-guessed).
Accepted papers (25)
Fetched from OpenReview (v2) on 2026-06-10.
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A Closer Look at Model Collapse: From a Generalization-to-Memorization Perspective
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An Empirical Exploration of Continual Unlearning for Image Generation
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Are Samples Extracted From Large Language Models Memorized?
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Bigger Isn’t Always Memorizing: Early Stopping Overparameterized Diffusion Models
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ContextLeak: Auditing Leakage in Private In-Context Learning Methods
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Counterfactual Influence as a Distributional Quantity
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Early-stopping Too Late? Traces of Memorization Before Overfitting in Generative Diffusion
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Evaluating Memorization in Parameter-Efficient Fine-tuning
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GenAI Copyright Evidence with Operational Meaning
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How Can I Publish My LLM Benchmark Without Giving the True Answers Away?
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Knowledge‑Distilled Memory Editing for Plug‑and‑Play LLM Alignment
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Language models’ activations linearly encode training-order recency
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Localizing and Mitigating Memorization in Image Autoregressive Models
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Low Resource Reconstruction Attacks Through Benign Prompts
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Low-Rank Adaptation Secretly Imitates Differentially Private SGD
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MAGIC: Diffusion Model Memorization Auditing via Generative Image Compression
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Mirage of Mastery: Memorization Tricks LLMs into Artificially Inflated Self-Knowledge
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Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs
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OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics
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OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature
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ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data
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Personal Information Parroting in Language Models
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Rethinking Memorization Measures in LLMs: Recollection vs. Counterfactual vs. Contextual Memorization
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Rote Learning Considered Useful: Generalizing over Memorized Data in LLMs
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Trade-offs in Data Memorization via Strong Data Processing Inequalities