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технические средства реабилитации инвалида перечень, GPT-3: Language Models are Few-Shot Learners - GitHub, o1、GPT4、GPT4o 这三个有什么区别？ - 知乎, chatgpt-chinese-gpt/ChatGPT-Chinese-version - GitHub, GitHub - openai/gpt-2: Code for the paper "Language Models are , GitHub - chatgpt-zh/chinese-chatgpt-guide: 国内如何使用 ChatGPT , GitHub - chatgpt-zh/chatgpt-china-guide: ChatGPT官网 | ChatGPT中文 , Using OpenAI GPT-4.1 in Copilot Chat - GitHub Docs.
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., GPT-4o的框架下，有小模，中模，大模三种尺寸，小模适合快速推理（其实什么都不适合，现在比较好用的小模是Flash002），中模适合大部分人的通用任务，包括数学，代码，查询差不多都在这一层，大模写作能力更好，情感理解更细腻，大模的存在并不能明显提升STEM领域的表现，但对那种规则不明确 .
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 , The dataset our GPT-2 models were trained on contains many texts with biases and factual inaccuracies, and thus GPT-2 models are likely to be biased and inaccurate as well. To avoid having samples mistaken as human-written, we recommend clearly labeling samples as synthetic before wide dissemination., ChatGPT 中文版支持 GPT-4 吗？ 是的，推荐的镜像站点均支持 GPT-4 模型。 使用中文版需要付费吗？ 部分站点提供免费额度，但高级功能可能需要付费。 使用中文版是否安全？ 推荐的站点均已通过基础隐私检测，但请勿输入敏感信息。, 本文提供全面的 ChatGPT 中文版使用指南，推荐国内镜像网站，支持GPT-4和GPT-o3，无需翻墙。 了解如何快速使用 ChatGPT 中文版 ，提升工作效率。 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 和 官网使用教程 ，帮助您快速上手 ChatGPT，无论是个人使用 , 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, 本文提供全面的 ChatGPT 中文版使用指南，推荐国内镜像网站，支持GPT-4和GPT-3.5，无需翻墙。了解如何快速使用 ChatGPT 中文版，提升工作效率。 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 .