16 citations · 16 across the 5 of their papers we have counts for
7 papers
MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulation
Yu Zhao, Hao Guan, Yongcheng Jing +2
Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While existing studies augment LLMs w…
rePIRL: Learn PRM with Inverse RL for LLM Reasoning
Xian Wu, Kaijie Zhu, Ying Zhang +2
Process rewards have been widely used in deep reinforcement learning to improve training efficiency, reduce variance, and prevent reward hacking. In LLM reasoning, existing works a…
Behavioral Fingerprinting of Large Language Models
Zehua Pei, Hui-Ling Zhen, Ying Zhang +5
Current benchmarks for Large Language Models (LLMs) primarily focus on performance metrics, often failing to capture the nuanced behavioral characteristics that differentiate them.…
PreMoE: Proactive Inference for Efficient Mixture-of-Experts
Zehua Pei, Ying Zhang, Hui-Ling Zhen +6
Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for deployment-specific specialization.…
Understanding Fact Recall in Language Models: Why Two-Stage Training Encourages Memorization but Mixed Training Teaches Knowledge
Ying Zhang, Benjamin Heinzerling, Dongyuan Li +1
While fine-tuning is the standard for injecting factual knowledge into large language models (LLMs), the mechanisms enabling reliable fact recall via unseen queries remain poorly u…
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
Jay Shah, Ganesh Bikshandi, Ying Zhang +3
Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. FlashAttention elaborated an appro…