45 citations · 53 across the 23 of their papers we have counts for
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cs.PF2023
Dissecting the Runtime Performance of the Training, Fine-tuning, and Inference of Large Language Models
Longteng Zhang, Xiang Liu, Zeyu Li +8
Large Language Models (LLMs) have seen great advance in both academia and industry, and their popularity results in numerous open-source frameworks and techniques in accelerating L…
cs.LG2023★ 2 cited
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
cs.CL2023★ 45 cited
Active Prompting with Chain-of-Thought for Large Language Models
Shizhe Diao, Pengcheng Wang, Yong Lin +3
The increasing scale of large language models (LLMs) brings emergent abilities to various complex tasks requiring reasoning, such as arithmetic and commonsense reasoning. It is kno…