2020年5月28日 · Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting.

2022年12月1日 · This paper provides an introductory survey to GPT-3. We cover some of the historical development behind this technology, some of the key features of GPT-3, and discuss the machine learning model...

2023年3月18日 · To conduct a comprehensive analysis of the capabilities of GPT series models, we select six representative models, comprising two GPT-3 series models (i.e., davinci and text-davinci-001) and four GPT-3.5 series models (i.e., code-davinci-002, text-davinci-002, text-davinci-003, and gpt-3.5-turbo).

GPT-3 is an autoregressive transformer model with 175 billion parameters.

2024年3月1日 · We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages.

2022年12月1日 · This paper provides an introductory survey to GPT-3. We cover some of the historical development behind this technology, some of the key features of GPT-3, and discuss the machine learning model and the datasets used.

2020年6月2日 · GPT-3证明了大型语言模型在少量示例或无示例的情况下能提升任务无关性能，甚至在某些任务上可与微调模型媲美。 通过1750亿参数的模型和大规模预训练，GPT-3在翻译、问答等任务上表现出色，减少了对领域标注数据的依赖。 尽管存在过拟合和数据污染问题，但其在生成任务上的优异性能展示了NLP领域的巨大潜力。 paper链接：https://arxiv.org/abs/2005.14165. github链接：https://github.com/openai/gpt-3. 通过对大量文本进行预训练，然后对特定任务进 …

This paper introduces GPT-3, an autoregressive language model with a groundbreaking scale of 175 billion parameters. The authors assess GPT-3's few-shot learning capabilities by subjecting it to various tasks without any gradient updates or fine-tuning.

合理假设底层交通网络的出行时间遵循多元高斯分布，我们提出了一种高斯过程路径规划（GP3）算法来计算先验最优路径作为RSP解决方案。 通过一系列等效的RSP问题变换，我们能够得到保证求解精度的多项式时间复杂度算法。 在各种规模的现实交通网络上进行的广泛实验结果证明了 GP3 相对于最先进算法的卓越性能。 This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP) regulated transportation networks.

GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art language processing model developed by OpenAI. It uses machine learning techniques to analyze and generate text, allowing it to perform a wide range of natural language tasks, such as language translation, text summarization, and question answering.