3 citations · 3 across the 5 of their papers we have counts for
5 papers
TreeReview: A Dynamic Tree of Questions Framework for Deep and Efficient LLM-based Scientific Peer Review
Yuan Chang, Ziyue Li, Hengyuan Zhang +6
While Large Language Models (LLMs) have shown significant potential in assisting peer review, current methods often struggle to generate thorough and insightful reviews while maint…
Quantization Meets Reasoning: Exploring LLM Low-Bit Quantization Degradation for Mathematical Reasoning
Zhen Li, Yupeng Su, Runming Yang +5
Large language models have achieved significant advancements in complex mathematical reasoning benchmarks, such as MATH. However, their substantial computational requirements prese…
HaLoRA: Hardware-aware Low-Rank Adaptation for Large Language Models Based on Hybrid Compute-in-Memory Architecture
Taiqiang Wu, Chenchen Ding, Wenyong Zhou +7
Low-rank adaptation (LoRA) is a predominant parameter-efficient finetuning method for adapting large language models (LLMs) to downstream tasks. Meanwhile, Compute-in-Memory (CIM)…
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models
Taiqiang Wu, Chaofan Tao, Jiahao Wang +3
Kullback-Leiber divergence has been widely used in Knowledge Distillation (KD) to compress Large Language Models (LLMs). Contrary to prior assertions that reverse Kullback-Leibler…
EZCrop: Energy-Zoned Channels for Robust Output Pruning
Rui Lin, Jie Ran, Dongpeng Wang +2
Recent results have revealed an interesting observation in a trained convolutional neural network (CNN), namely, the rank of a feature map channel matrix remains surprisingly const…