часть 2 преступление и наказание краткое содержание
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часть 2 преступление и наказание краткое содержание, 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, o1、GPT4、GPT4o 这三个有什么区别？ - 知乎, GitHub - openai/gpt-2: Code for the paper "Language Models are , 2025年最新ChatGPT中文版：国内免费直连指南（支持GPT-4，GPT-4o，无需翻墙）, chinese-chatgpt-mirrors/gpt-cn-guide - GitHub, ChatGPT 国内中文版｜免费ChatGPT镜像网站推荐（支持 ChatGPT/GPT-4o/o1/o3/o4）.
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 .
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 , 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., 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 和 官网使用教程 ，帮助您快速上手 ChatGPT，无论是个人使用还是专业 , 🛠️ 多模型兼容：集成 GPT-4o、o1、o3、Claude、Gemini、Grok 等主流AI模型，支持翻译、写作、问答等多场景 📋 国内优质 ChatGPT 镜像站点推荐 以下为精选的国内优质 ChatGPT 中文镜像站 ，带来流畅的中文AI体验：, 📢 一站式体验 ChatGPT 中文版，无需翻墙，畅享 GPT-4o/o1/o3（2025-06-24最新） 本项目专为中国用户打造，提供权威的 ChatGPT 中文版使用手册，系统整理国内主流的 ChatGPT 镜像网站及操作指引。无论您是学习、工作还是专业开发，都 , 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 .