61 citations · 133 across the 9 of their papers we have counts for
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cs.CL2023★ 61 cited
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
Cheng Li, Jindong Wang, Yixuan Zhang +6
Emotional intelligence significantly impacts our daily behaviors and interactions. Although Large Language Models (LLMs) are increasingly viewed as a stride toward artificial gener…
cs.LG2023★ 17 cited
ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
Zhewei Yao, Xiaoxia Wu, Cheng Li +2
Post-training quantization (PTQ) has emerged as a promising technique for mitigating memory consumption and computational costs in large language models (LLMs). However, a systemat…
cs.CL2023★ 6 cited
Understanding INT4 Quantization for Transformer Models: Latency Speedup, Composability, and Failure Cases
Xiaoxia Wu, Cheng Li, Reza Yazdani Aminabadi +2
Improving the deployment efficiency of transformer-based language models has been challenging given their high computation and memory cost. While INT8 quantization has recently bee…