101 citations · 101 across the 2 of their papers we have counts for
2 papers
cs.AI2026
HARC: Coupling Harmfulness and Refusal Directions for Robust Safety Alignment
Shei Pern Chua, Hao Wu, Qianli Ma +1
Understanding how aligned LLMs internally represent safety is critical for diagnosing alignment vulnerabilities, as it explains why jailbreaks succeed and informs the design of rob…
cs.CL2022★ 101 cited
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Guangxuan Xiao, Ji Lin, Mickael Seznec +3
Large language models (LLMs) show excellent performance but are compute- and memory-intensive. Quantization can reduce memory and accelerate inference. However, existing methods ca…