68 citations · 81 across the 17 of their papers we have counts for
4 papers · 1 filter
OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models
Shuai Wang, Liang Ding, Li Shen +3
Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP…
Revisiting Knowledge Distillation for Autoregressive Language Models
Qihuang Zhong, Liang Ding, Li Shen +3
Knowledge distillation (KD) is a common approach to compress a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, in the…
ROSE Doesn't Do That: Boosting the Safety of Instruction-Tuned Large Language Models with Reverse Prompt Contrastive Decoding
Qihuang Zhong, Liang Ding, Juhua Liu +2
With the development of instruction-tuned large language models (LLMs), improving the safety of LLMs has become more critical. However, the current approaches for aligning the LLMs…
Zero-Shot Sharpness-Aware Quantization for Pre-trained Language Models
Miaoxi Zhu, Qihuang Zhong, Li Shen +4
Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access…