3 papers
cs.LG2026
SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning
Si Qi Goh, Cap Dang Xuan Kiet, Tat-Jen Cham +1
The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of personally identifiable informa…
cs.LG2025
FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs
Si Qi Goh, Yongsen Zheng, Ziyao Liu +2
Machine unlearning (MU) seeks to eliminate the influence of specific training examples from deployed models. As large language models (LLMs) become widely used, managing risks aris…
cs.AI2024
AI Safety Landscape for Large Language Models: Taxonomy, State-of-the-art, and Future Directions
Chen Chen, Xueluan Gong, Ziyao Liu +3
AI Safety is an emerging area of critical importance to the safe adoption and deployment of AI systems. With the rapid proliferation of AI and especially with the recent advancemen…