Training language models to follow instructions with human feedback

🤖 Plain-English Summary

Making language models bigger does not inherently make them better at following a user's intent. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets.

🔑 Key Findings

• 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.

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