Training language models to follow instructions with human feedback

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.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user.
• 2 In other words, these models are not aligned with their users.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

This summary is based on publicly available metadata and abstract. For the full research paper, visit the original source: