Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation (2025.findings-acl)
Copied to clipboard
| Challenge: | Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit’s superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. |
| Approach: | They propose a self-distillation method that dynamically adjusts target logits to achieve a uniform probability for the target token. |
| Outcome: | Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit’s superior performance in balancing forget and retain objectives. |
Similar Papers
UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for unlearning large language models fine-tune by maximizing loss, but they are unstable . this creates instability, especially on larger datasets, leading to over-unlearning . |
| Approach: | They propose a novel unlearning method that leverages self-distillation to adjust logits . this method ensures smooth convergence and avoids catastrophic forgetting . |
| Outcome: | The proposed method achieves smooth convergence and avoids catastrophic forgetting even on large datasets and sequential unlearning requests. |
Sparse Logit Sampling: Accelerating Knowledge Distillation in LLMs (2025.acl-long)
Copied to clipboard
Anshumann Anshumann, Mohd Abbas Zaidi, Akhil Kedia, Jinwoo Ahn, Taehwak Kwon, Kangwook Lee, Haejun Lee, Joohyung Lee
| Challenge: | Knowledge distillation is a cost-effective technique to distill knowledge in Large Language Models, if the teacher output logits can be pre-computed and cached. |
| Approach: | They propose an importance-sampling-based method which provides unbiased estimates, preserves the gradient in expectation, and requires storing significantly sparser logits. |
| Outcome: | The proposed method enables faster training of student models with marginal overhead (10%) compared to cross-entropy based training, while maintaining competitive performance compared with full distillation. |
Balancing Forget Quality and Model Utility: A Reverse KL-Divergence Knowledge Distillation Approach for Better Unlearning in LLMs (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for unlearning large language models struggle with forget quality and model utility, leading to over-unlearning or partial unlearning. |
| Approach: | They propose a method that uses reverse KL-divergence based knowledge distillation for unlearning to achieve significant forget quality while maintaining model utility. |
| Outcome: | The proposed method outperforms existing methods in forget quality and model utility with larger unlearning datasets. |
SelFusion: Self-distillation for Diffusion Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing knowledge distillation methods for autoregressive large language models (LLMs) are not effective for reducing generation quality, but they can be useful for real-time applications. |
| Approach: | They propose a self-distillation framework that allows for effective KD without external teacher . they propose to use two modes of knowledge distillation to determine distillation direction . |
| Outcome: | The proposed framework outperforms existing methods with external teachers on instruction-following tasks. |
Direct Token Optimization: A Self-Contained Approach to Large Language Model Unlearning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for large language models (LLMs) rely on external resources such as auxiliary models, retain datasets, or even commercial AI services. |
| Approach: | They propose a self-contained unlearning approach that optimizes the token-level objectives to unlearn specific sequences without external resources. |
| Outcome: | The proposed approach improves the forget quality up to 16.8 over the latest benchmarks while maintaining comparable model utility. |
OBLIVIATE: Robust and Practical Machine Unlearning for Large Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) trained over corpora risk memorizing sensitive, copyrighted, or toxic content. |
| Approach: | They propose a framework that removes targeted data while preserving model utility. |
| Outcome: | The proposed framework resists membership inference attacks, minimizes impact on retained data, and maintains robustness across diverse scenarios. |
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation (2024.emnlp-main)
Copied to clipboard
KaShun Shum, Minrui Xu, Jianshu Zhang, Zixin Chen, Shizhe Diao, Hanze Dong, Jipeng Zhang, Muhammad Raza
| Challenge: | Experimental results show that a well-calibrated model is more reliable than a fine-tuned model due to “tuning-induced mis-calibration”. |
| Approach: | They propose a method which utilizes a small portion of teacher’s knowledge to obtain a reliable language model in a cost-efficient way. |
| Outcome: | The proposed method reduces the computational burden by utilizing teacher's knowledge to obtain a reliable language model in a cost-efficient way. |
Decoding-Unlearning: Fact Forgetting via Entropy-Guided Inference (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large-scale modeling memorize sensitive information . however, they are limited in real-world scenarios and require updating parameters . |
| Approach: | They propose a training-free, plug-and-play inference-time unlearning strategy that uses a probe to detect queries involving forgettable concepts and applies entropy-guided decoding to suppress target knowledge. |
| Outcome: | Experiments on MUSE, RWKU, and WMDP datasets show that SEGUE outperforms existing methods. |
Human-Inspired Obfuscation for Model Unlearning: Local and Global Strategies with Hyperbolic Representations (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for unlearning large language models struggle to balance effective forgetting with maintaining model utility. |
| Approach: | They propose a human-inspired unlearning framework that simulates forgetting on fuzzy data and represents them in hyperbolic and Euclidean spaces. |
| Outcome: | The proposed framework is able to forget sensitive content while maintaining the model’s language understanding, fluency, and benchmark performance. |
CAP: Controllable Alignment Prompting for Unlearning in LLMs (2026.acl-long)
Copied to clipboard
Zhaokun Wang, Jinyu Guo, Jingwen Pu, Hongli Pu, Meng Yang, Xunlei Chen, Jie Ou, Wenyi Li, Guangchun Luo, Wenhong Tian
| Challenge: | Existing methods for modifying parameters are unsystematic and rely on empirical experience. |
| Approach: | They propose a controllable alignment prompting for unlearning framework that decouples unlearning into a learnable prompt optimization process via reinforcement learning. |
| Outcome: | The proposed framework achieves precise, controllable unlearning without updating model parameters. |