OPTML-Group/Unlearn-Trace

[ICLR26] Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

24

stars

21

commits

Python

primary language

Apr 8, 2026

updated

large-language-model
llm
machine-learning
machine-unlearning
trustworthy-ai

README

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

preprint

Venue:ICLR 2026 License: MIT GitHub top language GitHub repo size GitHub stars

teaser
Figure 1: Schematic overview of unlearning trace detection.

This is the official code repository for the ICLR 2026 paper Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs.

News

  • 🎉 [Jan.26.2026] Our paper is accepted at ICLR 2026!
  • 🏆 [Jun.10.2025] Our paper’s short version accepted for Oral at MUGen@ICML’25!
  • 🔥 Check out our related ICLR 2026 paper: Safety Mirage, which proposes machine unlearning as a more robust alignment alternative for VLM safety fine-tuning.

Data Preperation

Please see Data.md.

LLM Unlearning

Please see Unlearn.md.

Installation

Please see Installation.md.

Response Generation and Data Split

Please see Response.md

Classifier Training and Evaluation

Please see Classification.md

Cite This Work

If you find out our paper or code helpful, please cite our work~

@article{chen2025unlearning,
  title={Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs},
  author={Chen, Yiwei and Pal, Soumyadeep and Zhang, Yimeng and Qu, Qing and Liu, Sijia},
  journal={arXiv preprint arXiv:2506.14003},
  year={2025}
}

Contributors

yiwei-chenn

21 commits

OPTML-Group/Unlearn-Trace

[ICLR26] Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

24

stars

21

commits

Python

primary language

Apr 8, 2026

updated

large-language-model
llm
machine-learning
machine-unlearning
trustworthy-ai

README

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

preprint

Venue:ICLR 2026 License: MIT GitHub top language GitHub repo size GitHub stars

teaser
Figure 1: Schematic overview of unlearning trace detection.

This is the official code repository for the ICLR 2026 paper Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs.

News

  • 🎉 [Jan.26.2026] Our paper is accepted at ICLR 2026!
  • 🏆 [Jun.10.2025] Our paper’s short version accepted for Oral at MUGen@ICML’25!
  • 🔥 Check out our related ICLR 2026 paper: Safety Mirage, which proposes machine unlearning as a more robust alignment alternative for VLM safety fine-tuning.

Data Preperation

Please see Data.md.

LLM Unlearning

Please see Unlearn.md.

Installation

Please see Installation.md.

Response Generation and Data Split

Please see Response.md

Classifier Training and Evaluation

Please see Classification.md

Cite This Work

If you find out our paper or code helpful, please cite our work~

@article{chen2025unlearning,
  title={Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs},
  author={Chen, Yiwei and Pal, Soumyadeep and Zhang, Yimeng and Qu, Qing and Liu, Sijia},
  journal={arXiv preprint arXiv:2506.14003},
  year={2025}
}

Contributors

yiwei-chenn

21 commits

Languages

Python

100.0%