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A review of the technology of OpenAI's ChatGPT InstructGPT: Training language models to follow instructions with human feedback https://arxiv.org/abs/2203.02155 Abstract Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent. #openai #chatgpt #pretrain #gpt3 #review #nlp #ethicalai #llm #bert #coursera #ml #course #rl #rewardmodel #ppo #instructgpt #sota #imagenet #inanutshell

是我目前用过的 无定语的 最强大的软件和工具。 但是，他的高额token消耗和成本让很多朋友望而却步，不敢尝试，或着 无法打开他的所有潜力， 今天我们介绍四种方法，在完全不牺牲性能的情况下，把成本降到10倍以下， 实现token自由， 让人人畅玩openclaw！ 一个视频全部掌握 ⏱️ 视频时间轴 (Timestamps): 0:23 Openclaw高成本的来源 1:21 QMD上下文管理 3:23 心跳优化 5:51 订阅制 6:37 流程优化 8:00 总结

干人工智能最怕的是什么？#人工智能 #ChatGPT #大语言模型 #讯飞星火 #文心一言 #模型训练 #ai算力需求六年暴增30万倍

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