работа водитель курьер личным авто свободный график
Главное меню:
Интересное
работа водитель курьер личным авто свободный график, GPT-3: Language Models are Few-Shot Learners - GitHub, ChatGPT中文版：国内访问指南（支持 GPT-4、GPT-4o - GitHub, chatgpt-chinese-gpt/ChatGPT-Chinese-version - GitHub, Using OpenAI GPT-4.1 in Copilot Chat - GitHub Docs, 国内如何使用 ChatGPT？最容易懂的 ChatGPT 介绍与教学指南, ChatGPT中文版:免费使用指南（支持GPT-4，GPT-4o，GPT-o1以及grok，无需翻墙）【5月最新】, 想免费体验 ChatGPT 中文版？支持 GPT-4 的国内 - GitHub.
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions – something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. 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. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general., 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. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model., ChatGPT中文版：国内访问指南（支持 GPT-4、GPT-4o、GPT-o1，无需翻墙）【5月持续更新】ChatGPT中文版、ChatGPT官网、ChatGPT网页版，本文提供完整的 ChatGPT 中文版使用指南，推荐国内可用的 ChatGPT 镜像网站，支持 GPT-4、GPT-4o，永久免费，无需翻墙，适合中文用户。 本项目旨在为用户提供一站式的 ChatGPT 中文 .
全面掌握 ChatGPT 中文版，无需翻墙即可体验 GPT-4 与多功能服务！ 本指南旨在为用户提供详尽的 ChatGPT 中文版使用指南，并整理国内常用的 ChatGPT镜像网站 与 官方使用教程，帮助您快速上手，不论是个人使用还是专业需求 , GPT-4.1 is one of those models and excels at coding tasks across the entire software development lifecycle, from initial design to bug fixes, maintenance to optimizations. For information about the capabilities of GPT-4.1, see the OpenAI documentation. GPT-4.1 is currently available in: Copilot Chat in Visual Studio Code, 这是一份全方位的指南，帮助您轻松使用 ChatGPT 中文版，无需科学上网即可体验 GPT-4 的全部功能！ 在本文中，您将了解如何通过推荐的镜像网站访问 ChatGPT 中文版、注册使用的具体步骤，以及常见问题的解答。, ChatGPT中文版免费使用指南（支持GPT-4，GPT-4o，GPT-o1以及grok，无需翻墙）【5月最新】 - chatgpt-chinese-gpt/chatgpt-freecn, ChatGPT4中文版免费使用指南（支持GPT-4，无需翻墙）想免费体验 ChatGPT 中文版？支持 GPT-4 的国内访问资源来了！ 我们为您整理了国内最全面的 ChatGPT中文版免费使用指南，包括高效的 GPT-4 中文访问教程，推荐无需翻墙即可使用的镜像网站和详细对比，让您轻松开启 AI 智能对话之旅。 - ChatGPT-CN-Guide , 本文提供完整的 ChatGPT 中文版使用指南，推荐国内可用的 ChatGPT 镜像网站，支持 GPT-4、GPT-4o，永久免费，无需翻墙，适合中文用户。 本项目旨在为用户提供一站式的 ChatGPT 中文版使用指南，同时整理了国内可用的 ChatGPT镜像网站 .