【Zero-shot-CoT】Large Language Models are Zero-shot Reasoners 有关few-shot,zero-shot,chain of thought,LLM的论文有好多,打算一篇一篇地看以下论文并为每一篇写一个或多或少的总结: 原文链接: [2205.11916] Large Language Models are Zero-Shot Reasoners (arxiv.org) ···思考··· 在few-shot-Co...
《Large Language Models are Zero-Shot Reasoners》0、Abstract这是一篇来自东京大学和谷歌的工作,关于预训练大型语言模型(Pretrained large language models, LLMs)的推理能力的探究。目前,LLMs被广泛运用在…
1、Large Language Models are Zero-Shot Reasoners 大型语言模型也是零样本推理器,只要在每个答案前面简单加上“让我们一步一步地思考(Let’s think step by step)”,MultiArith的准确率就能从17.7%提高到78.7% ,GPT-3的GSM8K从10.4%提高到40.7% 。
[TOC] > [Kojima T., Gu S. S., Reid M., Matsuo Y. and Iwasawa Y. Large language models are zero-shot reasoners. NIPS, 2022.](http://arxiv.org/abs/22
A research team from the University of Tokyo and Google Brain addresses this deficiency in their new paperLarge Language Models are Zero-Shot Reasoners, which demonstrates that LLMs can become decent zero-shot reasoners through the addition of a simple prompt —“Let’s think step by ...
Large Language Models Are Neurosymbolic Reasoners 机构: University of Liverpool, United Kingdom; Eindhoven University of Technology, Netherlands; University of Technology Sydney, Australia; University College London, United Kingdom 论文链接: https://arxiv.org/pdf/2401.09334.pdf ...
Large Language Models:语言模型(LM)是基于概率计算,旨在通过根据已经出现的单词来预测下一个(或缺失的)标记的概率。对于标准的语言模型,给定输入 和参数化的概率模型 ,我们的期望是最大化目标输出 的似然性,如下所示: 其中 表示第 个标记, 表示目标输出的长度。
Large language models are zero-shot reasoners. In: Proceedings of the 36th Conference on Neural Information Processing Systems. 2022, 22199–22213 Raman S S, Cohen V, Rosen E, Idrees I, Paulius D, Tellex S. Planning with large language models via corrective re-prompting. In: Proceedings ...
Large language models are zero-shot reasoners. Adv. Neural. Inf. Process. Syst. 35, 22199–22213 (2022). Google Scholar Wei, J. et al. Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural. Inf. Process. Syst. 35, 24824–24837 (2022). Google Scholar ...
Least-to-most prompting enables complex reasoning in large language models The 11th International Conference on Learning Representations (ICLR) (2023) Google Scholar 8 T. Kojima, S.S. Gu, M. Reid, Y. Matsuo, Y. Iwasawa Large language models are zero-shot reasoners Advances in Neural Informati...