·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Photonic reservoir computing with complex networks4h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control4h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents4h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks4h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models4h◆Creative Integration: A Decidable Criterion of Creativity4h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi4h◆Joint Optimization for Greedy Longest-match Tokenization4h◆Kimi K3: Open Frontier Intelligence4h◆The Few-shot Dilemma: Over-prompting Large Language Models4h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism4h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram4h◆StageGuard: Physiologically Constrained Sleep Staging4h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders4h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment4h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation4h◆Predicting Channel Closures in the Lightning Network with Machine Learning4h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures4h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback4h◆Cursor makes its biggest India push yet ahead of SpaceX acquisition with localized pricing4h◆Photonic reservoir computing with complex networks4h◆XS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control4h◆Agentic Permissions Policy Algebra for Taint Confinement in LLM Agents4h◆Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks4h◆The One-Word Census: Answer-Choice Conformity Across 44 Language Models4h◆Creative Integration: A Decidable Criterion of Creativity4h◆BERT-based Models vs. Large Language Models for Low-Resource Named Entity Recognition: A Comparative Study on Marathi4h◆Joint Optimization for Greedy Longest-match Tokenization4h◆Kimi K3: Open Frontier Intelligence4h◆The Few-shot Dilemma: Over-prompting Large Language Models4h◆Speculative Pipeline Decoding: Higher-Accuracy Drafting with Hidden Latency via Pipeline Parallelism4h◆Bayesian Complete-Pooling in Cross-Subject Classification for Motor Imagery Electroencephalogram4h◆StageGuard: Physiologically Constrained Sleep Staging4h◆Soft-Constrained Optimization of Latent Space in Variational Autoencoders4h◆Beyond Error-vs-Discard Characteristic: Toward Stable and Reliable Evaluation for Face Image Quality Assessment4h◆Analyzing the Importance of Blank for CTC-Based Knowledge Distillation4h◆Predicting Channel Closures in the Lightning Network with Machine Learning4h◆Evaluation of Blood Vessel Segmentation Methods on Hard-to-Detect Vascular Structures4h◆MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedback4h◆
News/Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning
arxiv
PublishedMay 7, 2026 at 4:00 AM
—neutral

Unlearning What Matters: Token-Level Attribution for Precise Language Model Unlearning

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2605.00364v2 Announce Type: replace Abstract: Machine unlearning has emerged as a critical capability for addressing privacy, safety, and regulatory concerns in large language models (LLMs). Existing methods operate at the sequence level, applying uniform updates across all tokens despite only

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Mentioned models
03
  • 01
    LLMs
  • 02
    TOFU
  • 03
    WMDP
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-unlearning#language-models#privacy#safety

No replies yet. Be first.

Mentioned models
03
  • 01
    LLMs
  • 02
    TOFU
  • 03
    WMDP
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#machine-unlearning#language-models#privacy#safety

Related coverage

More from ARXIV
arxivPhotonic reservoir computing with complex networks4harxivXS-VLA: Coupling Coarse-grained Spatial Distillation with Latent Flow Matching for Lightweight Robotic Control4harxivAgentic Permissions Policy Algebra for Taint Confinement in LLM Agents4harxivBeyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks4h
The Bubble Brief
WEEKLY

Read machine-unlearning insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews