"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 NAME_10
Amanda AskellyPeter Welinder NAME_11 Christianoy NAME_12 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."

Above article lists 10 questions.

1. human-written prompts
2. OpenAI API prompts
3. Collect a dataset of labeler demonstrations of the desired model behavior.
4. Further ﬁne-tune this supervised model using reinforcement learning from human feedback.
5. Call the resulting model InstructGPT.
6. 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.
7. InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.
8. Even though InstructGPT still makes simple mistakes, our results show that ﬁne-tuning with human feedback is a promising direction for aligning language m