Summarize and give 10 questions:"Paper title: Training language models to follow instructions with human feedback authors: NAME_1 OuyangJeff WuXu JiangDiogo AlmeidaCarroll L. NAME_2 NAME_3 MishkinChong NAME_4 Sandhini Agarwal NAME_5 NAME_6 NAME_7 NAME_8 NAME_9 Maddie NAME_10 NAME_11 AskellyPeter NAME_12 NAME_13 Christianoy Jan LeikeRyan LoweOpenAI 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." Provide exactly two different responses that are separated with 6 asterisk symbols: ******.
user