Hugging Face Trending Papers June 9, 2026

Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning

Read the original on Hugging Face Trending Papers →

Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities. Many unlearning objectives focus on suppressing undesired answers, while recent target-guided variants specify replacement behavior but still leave update locality largely unconstrained.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 10

Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning

arXiv:2606. 10989v1 Announce Type: new Abstract: Large language model unlearning aims to suppress designated undesirable knowledge while preserving benign capabilities.

By Bocheng Ju, Jianhua Wang, Chengliang Liu, Xiaolin Chang

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

arXiv:2606. 06320v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities.

By Gizem Y\"uce, Giorgos Nikolaou, Nicolas Flammarion

llms

arXiv Machine Learning

1d ago

SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning

arXiv:2608. 16249v1 Announce Type: new Abstract: Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target knowledge and preserving general utility.

By Jaewan Choi, Junyoung Yang, Sangdon Park

llms benchmarks

arXiv Machine Learning

Jun 16

Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

arXiv:2601. 18699v2 Announce Type: replace Abstract: Sequential fine-tuning of Large Language Models (LLMs) adaptation to target tasks often triggers catastrophic forgetting, where the acquisition of novel target skills degrades ancestral capabilities.

By Gustav Olaf Yunus Laitinen-Fredriksson Lundstrom-Imanov

llms fine-tuning benchmarks safety

arXiv Machine Learning

Jun 5

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

arXiv:2605. 07482v2 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full retraining.

By Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei, Mohammad Rostami, Jesse Thomason, Robin Jia

llms efficiency benchmarks

arXiv AI

5d ago

Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds

arXiv:2608. 13069v1 Announce Type: new Abstract: Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants.

By Lucia Mal\'i\v{c}kov\'a

llms reinforcement-learning fine-tuning benchmarks safety

Related stories

Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning

Mechanistic Analysis of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds