all InfoSec news
Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
April 3, 2024, 4:10 a.m. | Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares, David S\'anchez, Josep Domingo-Ferrer, Guillem Collell, Kuan Eeik Tan
cs.CR updates on arXiv.org arxiv.org
Abstract: The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations for forgetting include privacy protection, copyright protection, elimination of biases and discrimination, and prevention of harmful content generation. Effective digital forgetting has to be effective (meaning how well the new model has forgotten the undesired knowledge/behavior), retain the performance of the original model on the desirable tasks, …
arxiv biases copyright copyright protection cs.ai cs.cr cs.lg digital discrimination knowledge language language models large prevention privacy protection survey
More from arxiv.org / cs.CR updates on arXiv.org
Proactive Detection of Voice Cloning with Localized Watermarking
2 days, 15 hours ago |
arxiv.org
NFT Wash Trading: Direct vs. Indirect Estimation
2 days, 15 hours ago |
arxiv.org
Backdoor Attack with Sparse and Invisible Trigger
2 days, 15 hours ago |
arxiv.org
Jobs in InfoSec / Cybersecurity
CyberSOC Technical Lead
@ Integrity360 | Sandyford, Dublin, Ireland
Cyber Security Strategy Consultant
@ Capco | New York City
Cyber Security Senior Consultant
@ Capco | Chicago, IL
Senior Security Researcher - Linux MacOS EDR (Cortex)
@ Palo Alto Networks | Tel Aviv-Yafo, Israel
Sr. Manager, NetSec GTM Programs
@ Palo Alto Networks | Santa Clara, CA, United States
SOC Analyst I
@ Fortress Security Risk Management | Cleveland, OH, United States