rethinking models - Servistopauto リップル https://servistopauto.ru/ rethinking models - Servistopauto リップル true https://servistopauto.ru/rethinking-models/185-Rethinking-driving-world-model-as-synthetic-data-generator-for-perception-tasks-100-sajin.html trinitydoramy rethinking models Thu, 19 Feb 2026 20:32:20 +0300

Rethinking driving world model as synthetic data generator for perception tasks (100) 사진

2107.04259] Unity Perception: Generate Synthetic Data for Computer Vision

A GAN-Based Framework with Dynamic Adaptive Attention for Multi-Class Image Segmentation in Autonomous Driving

BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models - MarkTechPost

Generative AI Meets Computer Vision | API4AI | Medium

Flexible multitask deep learning for lane-aware panoptic segmentation in autonomous driving [PeerJ]

Review 2 Report........ | PDF | Computer Vision | Artificial Intelligence

Important LLM Papers for the Week From 10/11 To 16/11

Generate synthetic datasets and scenarios by learning from the real world

ICCV 2025 Boundary-Pushing CV Research | Voxel51

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

CMES | Free Full-Text | Deep Learning and Federated Learning in Human Activity Recognition with Sensor Data: A Comprehensive Review

Simulately

An interactive segmentation-based method for seismic facies annotation and segmentation | Scientific Reports

Important LLM Papers for the Week From 28/07 to 02/08

Simulately

Federated learning system on autonomous vehicles for lane segmentation | Scientific Reports

Transfer Your Perspective: Controllable 3D Generation from Any Viewpoint in a Driving Scene

End-to-end Autonomous Driving: Challenges and Frontiers - Reading Collections

Foundation models and intelligent decision-making: Progress, challenges, and perspectives: The Innovation

Google AI Introduces Learn-by-Interact: A Data-Centric Framework for Adaptive and Efficient LLM Agent Development - MarkTechPost

Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

From Perception to Action: The Role of World Models in Embodied AI Systems - MarkTechPost

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Important LLM Papers for the Week From 10/11 To 16/11

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

The Art of Synthetic Wisdom: Teaching Machines to Think in the Absence of Data | by R. Thompson (PhD) | Towards AI

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Learning naturalistic driving environment with statistical realism | Nature Communications

PDF) SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration

Frontiers | A survey of decision-making and planning methods for self- driving vehicles

分享下我们团队最新工作Dream4Drive【小米汽车x北大】 - 知乎

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Simulately

Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

PDF) Drive Like a Human: Rethinking Autonomous Driving with Large Language Models

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Synthetic data generation for the continuous development ...

Important LLM Papers for the Week From 10/11 To 16/11

Large language models empowered agent-based modeling and simulation: a survey and perspectives | Humanities and Social Sciences Communications

Paper page - GenieDrive: Towards Physics-Aware Driving World Model with 4D Occupancy Guided Video Generation

Simulately

A recent T-RO paper introduces a model-simplification framework for efficient deformable-object manipulation: task-conditioned action-space reduction plus simplified #dynamics lets faster planning for cloth folding & rope shaping. Paper title: Goal ...

Daily Papers - Hugging Face

A Critical Analysis of Generative AI: Challenges, Opportunities, and Future Research Directions | Archives of Computational Methods in Engineering | Springer Nature Link

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Papers by Bohan Zeng

Certified Robustness in Automated Driving Perception: A Review | Automotive Innovation

AeroVerse-Review: Comprehensive survey on aerial embodied vision-and-language navigation

Synchronization, Optimization, and Adaptation of Machine Learning Techniques for Computer Vision in Cyber-Physical Systems: A Comprehensive Analysis[v2] | Preprints.org

Toward sustainable production: a synthetic dataset framework to accelerate quality control via generative and predictive AI | The International Journal of Advanced Manufacturing Technology

Multimodal Language Models in Autonomous Driving | PDF | Artificial Intelligence | Intelligence (AI) & Semantics

ICCV 2025 Boundary-Pushing CV Research | Voxel51

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Automated Prompt Engineering: Leveraging Synthetic Data and Meta-Prompts for Enhanced LLM Performance - MarkTechPost

Certified Robustness in Automated Driving Perception: A Review | Automotive Innovation

Foundation models and intelligent decision-making: Progress, challenges, and perspectives: The Innovation

Vision Language Models in Autonomous Driving: A Survey and Outlook

3D Environment Generation from Sparse Inputs for Automated Driving Function Development[v1] | Preprints.org

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

Synchronization, Optimization, and Adaptation of Machine Learning Techniques for Computer Vision in Cyber-Physical Systems: A Comprehensive Analysis[v2] | Preprints.org

Synthetic Data Generation Using Large Language Models: Advances in Text and Code

BARE: A Synthetic Data Generation AI Method that Combines the Diversity of Base Models with the Quality of Instruct-Tuned Models - MarkTechPost

Large language models empowered agent-based modeling and simulation: a survey and perspectives | Humanities and Social Sciences Communications

Simulately

Ever wondered how the most complex AI systems - like deep neural networks - start with something as simple as a straight line? The Perceptron, the

This AI Research Introduces FollowNet: A Comprehensive Benchmark Dataset for Car-Following Behavior Modeling - MarkTechPost

PDF) WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

2312.00438] Dolphins: Multimodal Language Model for Driving

Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective

Important LLM Papers for the Week From 27/10 To 01/11

Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? (Google DeepMind, July 2024) Paper: [https://arxiv.org/abs/2406.13121](https://arxiv.org/abs/2406.13121) Abstract:

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Generative AI for Autonomous Driving: Frontiers and Opportunities

Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

publications | Aleksandr I. Panov

Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling (Shanghai AI Laboratory, February 2025) Paper: [https://arxiv.org/abs/2502.06703](https://arxiv.org/abs/2502.06703) Abstract:

🧠 Top 5 Trends in Data Annotation Heading Into 2026 In 2026, the focus is shifting from “more labels” to smarter, scalable, and production-ready data pipelines. Here are the five key data

Multi‐domain autonomous driving dataset: Towards enhancing the generalization of the convolutional neural networks in new environments - Khosravian - 2023 - IET Image Processing - Wiley Online Library

🧠 Top 5 Trends in Data Annotation Heading Into 2026 In 2026, the focus is shifting from “more labels” to smarter, scalable, and production-ready data pipelines. Here are the five key data

Unleashing the potential of prompt engineering for large language models: Patterns

Literature Review] Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models

Robust motion planning for mobile robots under attacks against obstacle localization | Robotica | Cambridge Core

Generate synthetic datasets and scenarios by learning from the real world

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

Vision tasks in clear and rain-augmented images. Our synthetic rain... | Download Scientific Diagram

OS-Genesis: A Novel GUI Data Synthesis Pipeline that Reverses the Conventional Trajectory Collection Process - MarkTechPost

Road surface semantic segmentation for autonomous driving [PeerJ]

4x RGT_S - OpenModelDB

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

PDF) Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities

Multi‐domain autonomous driving dataset: Towards enhancing the generalization of the convolutional neural networks in new environments - Khosravian - 2023 - IET Image Processing - Wiley Online Library

Road surface semantic segmentation for autonomous driving [PeerJ]

Generative AI in depth: A survey of recent advances, model variants, and real-world applications | Journal of Big Data | Springer Nature Link

Closing the Domain Gap: Can Pseudo-Labels from Synthetic UAV Data Enable Real-World Flood Segmentation?

CV计算机视觉每日开源代码Paper with code速览-2025.11.15 - 知乎

Important LLM Papers for the Week From 27/10 To 01/11

Artificial Intelligence for Cardiovascular Care in Action: From Learning to Implementation in Health Systems | JACC: Advances

]]> true https://servistopauto.ru/rethinking-models/186-Rethinking-entropy-regularization-in-large-reasoning-models-89-sajin.html trinitydoramy rethinking models Thu, 19 Feb 2026 20:32:20 +0300

Rethinking entropy regularization in large reasoning models (89) 사진

Theoretical Perspectives on Knowledge Distillation: A Review - Liu - 2025 - WIREs Computational Statistics - Wiley Online Library

Do Reasoning Models Really Need Transformers?: Researchers from TogetherAI, Cornell, Geneva, and Princeton Introduce M1—A Hybrid Mamba-Based AI that Matches SOTA Performance at 3x Inference Speed - MarkTechPost

Fin-R1: A Specialized Large Language Model for Financial Reasoning and Decision-Making - MarkTechPost

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

Tractable Regularization of Probabilistic Circuits

Conditional Domain Adaptation with α-Rényi Entropy Regularization and Noise-Aware Label Weighting[v1] | Preprints.org

Conditional Domain Adaptation with α-Rényi Entropy Regularization and Noise-Aware Label Weighting[v1] | Preprints.org

Internal Consistency and Self-Feedback in Large Language Models: A Survey (Renmin University of China, July 2024) Paper: [https://arxiv.org/abs/2407.14507](https://arxiv.org/abs/2407.14507) Abstract:

Important LLM Papers for the Week From 27/10 To 01/11

Literature Review] Rethinking LLM Human Simulation: When a Graph is What You Need

EPO: Entropy-Regularized Policy Optimization for LLM Agents Reinforcement Learning | by Dixon | Medium

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

Beyond Aha Moments: Structuring Reasoning in Large Language Models - MarkTechPost

PDF) Spurious Rewards: Rethinking Training Signals in RLVR

Simulately

上海AI lab 提出SIREN：重新思考RLVR 中的熵正则- 知乎

Literature Review] Review of Inference-Time Scaling Strategies: Reasoning, Search and RAG

Literature Review] Rethinking Entropy Regularization in Large Reasoning Models

Entropy-Regularized Attention for Explainable Histological Classification with Convolutional and Hybrid Models

Reinforcement Learning with Verifiable Rewards

Contextual Knowledge Infusion via Iterative Semantic Tracing for Vision–Language Understanding[v1] | Preprints.org

The State of Reinforcement Learning for LLM Reasoning

Reward Modeling | RLHF Book by Nathan Lambert

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

Literature Review] Clip-Low Increases Entropy and Clip-High Decreases Entropy in Reinforcement Learning of Large Language Models

Multi-access edge computing scheduling optimization model for remote education under 6G network environment based on reinforcement learning | Scientific Reports

ReVisual-R1: An Open-Source 7B Multimodal Large Language Model (MLLMs) that Achieves Long, Accurate and Thoughtful Reasoning - MarkTechPost

Depth-Breadth Synergy in RLVR: Unlocking LLM Reasoning Gains with Adaptive Exploration

Important LLM Papers for the Week From 28/07 to 02/08 | by Youssef Hosni | Towards AI

The State of Reinforcement Learning for LLM Reasoning

ICLR2023 Statistics

Rethinking Data Use in Large Language Models

Rethinking Chain-of-Thought from the Perspective of Self-Training

The State of Reinforcement Learning for LLM Reasoning

Important LLM Papers for the Week From 28/07 to 02/08

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective | International Journal of Computer Vision

Conditional Domain Adaptation with α-Rényi Entropy Regularization and Noise-Aware Label Weighting[v1] | Preprints.org

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

Yafu Li (@yafuly) / Posts / X

Can Large Reasoning Models Self-Train?

Reinforcement Learning from One Example? | Towards Data Science

The State of Reinforcement Learning for LLM Reasoning

Off-Policy Reinforcement Learning RL with KL Divergence Yields Superior Reasoning in Large Language Models - MarkTechPost

上海AI lab 提出SIREN：重新思考RLVR 中的熵正则- 机器学习POD

论文评述] From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence

Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective | International Journal of Computer Vision

Aligning machine and human visual representations across abstraction levels | Nature

上海AI lab 提出SIREN：重新思考RLVR 中的熵正则- 知乎

Learning from models beyond fine-tuning | Nature Machine Intelligence

无监督RL的粗略分析- 知乎

Entropy | March 2025 - Browse Articles

Important LLM Papers for the Week From 28/07 to 02/08 | by Youssef Hosni | Towards AI

Shamane Siri (@GShamane) / Posts / X

Emergent neuronal mechanisms mediating covert attention in convolutional neural networks | PNAS

The State of Reinforcement Learning for LLM Reasoning

The State of Reinforcement Learning for LLM Reasoning

Structure Trumps Size: Rethinking Data Quality for LLM Reasoning

Today I came across an interesting phenomenon in LLM reasoning on X. Some models actually switch languages mid-problem—not because they are confused, but because they are optimizing. Why? Because Chinese is computationally

Reasoning Training & Inference-Time Scaling | RLHF Book by Nathan Lambert

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

AREAL: Accelerating Large Reasoning Model Training with Fully Asynchronous Reinforcement Learning - MarkTechPost

Entropy-Regularized Attention for Explainable Histological Classification with Convolutional and Hybrid Models

Literature Review] The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models

NeurIPS Poster Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?

A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions - Naser - 2025 - WIREs Data Mining and Knowledge Discovery - Wiley Online Library

Toward large reasoning models: A survey of reinforced reasoning with large language models: Patterns

Important LLM Papers for the Week From 28/07 to 02/08 | by Youssef Hosni | Towards AI

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

Scalable MatMul-free Language Modeling: Ternary Weights and MLGRU | Devansh Devansh posted on the topic | LinkedIn

Beyond the Mean: How Quantile Baselines Tame Entropy in AI Reasoning | by GlitchQ | Medium

Tractable Regularization of Probabilistic Circuits

Advancing Medical Reasoning with Reinforcement Learning from Verifiable Rewards (RLVR): Insights from MED-RLVR - MarkTechPost

Reasoning Training & Inference-Time Scaling | RLHF Book by Nathan Lambert

DeepSeek-R1 Special Edition

RETHINKING ENTROPY REGULARIZATION IN LARGE REASONING MODELS

PDF) Rethinking Entropy Regularization in Large Reasoning Models

Rethinking statistical learning theory: learning using statistical invariants | Machine Learning

Tractable Regularization of Probabilistic Circuits

Important LLM Papers for the Week From 27/10 To 01/11 | by Youssef Hosni | Level Up Coding

Multitask Learning 1997–2024: Part I Fundamentals · Issue 7.3, Summer 2025

Explore Data Left Behind in Reinforcement Learning for Reasoning Language Models

Image Super-Resolution Achieves Efficiency Via Individualized Exploratory Attention, Rethinking Token Similarities

Predictive maintenance in Industry 4.0: a survey of planning models and machine learning techniques [PeerJ]

Differential Smoothing Mitigates Sharpening and Improves LLM Reasoning

Rethinking KL Regularization in RLHF: From Value Estimation to Gradient Optimization

Lokesh Mishra (@exomishra) / Posts / X

论文评述] Boosting RL-Based Visual Reasoning with Selective Adversarial Entropy Intervention

]]> true https://servistopauto.ru/rethinking-models/187-Rethinking-machine-unlearning-for-large-language-models-99-sajin.html trinitydoramy rethinking models Thu, 19 Feb 2026 20:32:20 +0300

Rethinking machine unlearning for large language models (99) 사진

Rethinking LLM Memorization – Machine Learning Blog | ML@CMU | Carnegie Mellon University

Large Language Models for Recommendation: Progresses and Future Direction

Rethinking LLM Training: The Promise of Inverse Reinforcement Learning Techniques - MarkTechPost

Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning

ICML Poster Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond

Trustworthy LLM and an Unlearning Approach

LLM Unlearning on Noisy Forget Sets: A Study of Incomplete, Rewritten, and Watermarked Data

Learn to Forget: Machine Unlearning via Neuron Masking

Machine Unlearning

A CLOSER LOOK AT MACHINE UNLEARNING FOR LARGE LANGUAGE MODELS-CSDN博客

In December of 2024, Anthropic released a research paper about a new LLM phenomenon called Alignment Faking. Large Language Models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) might fake alignment

Digital forgetting in large language models: a survey of unlearning methods | Artificial Intelligence Review | Springer Nature Link

Machine Unlearning

Learn to Forget: Machine Unlearning via Neuron Masking

A Survey on Unlearning in Large Language Models

Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Unlearning as multi-task optimization: A normalized gradient difference approach with an adaptive learning rate

A General Framework to Enhance Fine-tuning-based LLM Unlearning

Knowledge-enhanced large language models for automatic lesson plan generation | Humanities and Social Sciences Communications

Digital forgetting in large language models: a survey of unlearning methods | Artificial Intelligence Review | Springer Nature Link

ICML Poster Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond

Paper page - CLEAR: Character Unlearning in Textual and Visual Modalities

Jinghan Jia

Literature Review] Simplicity Prevails: Rethinking Negative Preference Optimization for LLM Unlearning

Literature Review] MUSE: Machine Unlearning Six-Way Evaluation for Language Models

Machine Unlearning in Large Language Models: A Comprehensive Survey with Empirical Insights from the Qwen 1.5 1.8B Model — LessWrong

Large Language Model Unlearning

Jogging the Memory of Unlearned LLMs Through Targeted Relearning Attacks

Rethinking Data Use in Large Language Models

Rethinking machine unlearning for large language models by Sijia Liu & Yuanshun Yao & Jinghan Jia & Stephen Casper & Nathalie Baracaldo & Peter Hase & Yuguang Yao & Chris Yuhao Liu

Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning

A survey on large language models unlearning: taxonomy, evaluations, and future directions | Artificial Intelligence Review | Springer Nature Link

Industrial applications of large language models | Scientific Reports

PDF] RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models | Semantic Scholar

PDF) Rethinking Post-Unlearning Behavior of Large Vision-Language Models

Rethinking Data Use in Large Language Models

PDF] Machine Unlearning of Pre-trained Large Language Models | Semantic Scholar

Paper page - CLEAR: Character Unlearning in Textual and Visual Modalities

Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning

Machine Unlearning in Large Language Models: A Comprehensive Survey with Empirical Insights from the Qwen 1.5 1.8B Model — LessWrong

ICML Poster Flexible, Efficient, and Stable Adversarial Attacks on Machine Unlearning

Rethinking Machine Unlearning for Large Language Models - 智源社区论文

Towards Efficient Machine Unlearning with Data Augmentation: Guided Loss-Increasing (GLI) to Prevent the Catastrophic Model Util

Decoupling Memories, Muting Neurons: Towards Practical Machine Unlearning for Large Language Models

SoK: Machine Unlearning for Large Language Models

Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning | by Raul Vizcarra Chirinos | TDS Archive | Medium

Trustworthy LLM and an Unlearning Approach

爱可可AI 前沿推介(2.16) - 知乎

Literature Review] OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models

Decoupling Memories, Muting Neurons: Towards Practical Machine Unlearning for Large Language Models

Can LLMs Unlearn? Part 4: The Technical Complexity of it All (continued)

LLM Unlearning via Neural Activation Redirection

Hierarchical Federated Unlearning for Large Language Models

Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning | by Raul Vizcarra Chirinos | TDS Archive | Medium

Beyond Aha Moments: Structuring Reasoning in Large Language Models - MarkTechPost

RETHINKING LLM UNLEARNING OBJECTIVES: A GRADIENT PERSPECTIVE AND GO BEYOND

Fine-tuning large language models for domain adaptation: exploration of training strategies, scaling, model merging and synergistic capabilities | npj Computational Materials

Learning to Unlearn: Why Data Scientists and AI Practitioners Should Understand Machine Unlearning | Towards Data Science

LLM Unlearning via Neural Activation Redirection

RETHINKING LLM UNLEARNING OBJECTIVES: A GRADIENT PERSPECTIVE AND GO BEYOND

Industrial applications of large language models | Scientific Reports

Decoupling Memories, Muting Neurons: Towards Practical Machine Unlearning for Large Language Models

LLM Unlearning with LLM Beliefs

PDF] RKLD: Reverse KL-Divergence-based Knowledge Distillation for Unlearning Personal Information in Large Language Models | Semantic Scholar

A survey of security and privacy issues of machine unlearning - Chen - 2025 - AI Magazine - Wiley Online Library

Harnessing large language models for data-scarce learning of polymer properties | Nature Computational Science

Vision-R1: Redefining Reinforcement Learning for Large Vision-Language Models - MarkTechPost

Literature Review] Offset Unlearning for Large Language Models

Rethinking LLM Memorization – Machine Learning Blog | ML@CMU | Carnegie Mellon University

Trustworthy LLM and an Unlearning Approach

A General Framework to Enhance Fine-tuning-based LLM Unlearning

PDF] RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models | Semantic Scholar

Large Language Models for Recommendation: Progresses and Future Direction

Major LLMs Have the Capability to Pursue Hidden Goals, Researchers Find - InfoQ

Machine Unlearning for Trustworthy AI: A Systematic Review of Techniques, Challenges, and Applications | Archives of Computational Methods in Engineering | Springer Nature Link

AI Blogs | AI Insights | AI Trends | AI Articles | AI News

PDF) SoK: Machine Unlearning for Large Language Models

Detecting Pretraining Data from Large Language Models

Machine Unlearning for Trustworthy AI: A Systematic Review of Techniques, Challenges, and Applications | Archives of Computational Methods in Engineering | Springer Nature Link

Machine Unlearning in Large Language Models: A Comprehensive Survey with Empirical Insights from the Qwen 1.5 1.8B Model — LessWrong

PDF) Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models

PDF] Rethinking machine unlearning for large language models | Semantic Scholar

MITIGATING MEMORIZATION IN LANGUAGE MODELS

Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling (Shanghai AI Laboratory, February 2025) Paper: [https://arxiv.org/abs/2502.06703](https://arxiv.org/abs/2502.06703) Abstract:

Rethinking Entity-level Unlearning for Large Language Models | AI Research Paper Details

🚨 Important + fun collaboration on this new position paper ➡️➡️

Learn to Forget: Machine Unlearning via Neuron Masking

Literature Review] Editing as Unlearning: Are Knowledge Editing Methods Strong Baselines for Large Language Model Unlearning?

PDF) The gains do not make up for the losses: a comprehensive evaluation for safety alignment of large language models via machine unlearning

Can LLMs Unlearn? Part 4: The Technical Complexity of it All (continued)

A Survey of Machine Unlearning Author Published Journal Title Version DOI Rights statement Downloaded from Funder(s) Grant ident

Efficient Knowledge Graph Unlearning with Zeroth-order Information | Proceedings of the 34th ACM International Conference on Information and Knowledge Management

Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning

Jeena NLP Project | PDF | Cognitive Science

Innovative Approaches in Machine Unlearning: Insights and Breakthroughs from the first NeurIPS Unlearning Competition on Efficient Data Erasure - MarkTechPost

论文评述] Rethinking Machine Unlearning in Image Generation Models

A General Framework to Enhance Fine-tuning-based LLM Unlearning

论文评述] Machine Unlearning in Large Language Models

]]>