3 papers
cs.CL2025
Unlearning Sensitive Information in Multimodal LLMs: Benchmark and Attack-Defense Evaluation
Vaidehi Patil, Yi-Lin Sung, Peter Hase +3
LLMs trained on massive datasets may inadvertently acquire sensitive information such as personal details and potentially harmful content. This risk is further heightened in multim…
cs.LG2025
Task-Circuit Quantization: Leveraging Knowledge Localization and Interpretability for Compression
Hanqi Xiao, Yi-Lin Sung, Elias Stengel-Eskin +1
Post-training quantization (PTQ) reduces a model's memory footprint by mapping full precision weights into low bit weights without costly retraining, but can degrade its downstream…
cs.LG2025
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
Yi-Lin Sung, Prateek Yadav, Jialu Li +2
Layer-wise quantization is a key technique for efficiently compressing large models without expensive retraining. Previous methods typically quantize the weights of each layer by "…