We’ve trained language models that are much better at following user intentions than GPT-3 while also making them more truthful and less toxic, using techniques developed through our alignment research. These InstructGPT models, which are trained with humans in the loop, are now deployed as the default language models on our API.
The OpenAI API is powered by GPT-3 language models which can be coaxed to perform natural language tasks using carefully engineered text prompts. But these models can also generate outputs that are untruthful, toxic, or reflect harmful sentiments. This is in part because GPT-3 is trained to predict the next word on a large dataset of Internet text, rather than to safely perform the language task that the user wants. In other words, these models aren’t aligned with their users.
We only use prompts submitted through the Playground to an earlier version of the InstructGPT models that was deployed in January 2021. Our human annotators remove personal identifiable information from all prompts before adding it to the training set.
our labelers provide demonstrations of the desired model behavior, and rank several outputs from our models. We then use this data to fine-tune GPT-3.
The resulting InstructGPT models are much better at following instructions than GPT-3. They also make up facts less often, and show small decreases in toxic output generation. Our labelers prefer outputs from our 1.3B InstructGPT model over outputs from a 175B GPT-3 model, despite having more than 100x fewer parameters. At the same time, we show that we don’t have to compromise on GPT-3’s capabilities, as measured by our model’s performance on academic NLP evaluations.
These InstructGPT models, which have been in beta on the API for more than a year, are now the default language models accessible on our API.
The InstructGPT models deployed in the API are updated versions trained using the same human feedback data. They use a similar but slightly different training method that we will describe in a forthcoming publication.
We believe that fine-tuning language models with humans in the loop is a powerful tool for improving their safety and reliability, and we will continue to push in this direction.
This is the first time our alignment research, which we’ve been pursuing for severalyears,123 has been applied to our product. Our work is also related to recent research that fine-tunes language models to follow instructions using academic NLP datasets, notably FLAN4 and T0.5 A key motivation for our work is to increase helpfulness and truthfulness while mitigating the harms and biases of language models.678910 Some of our previous research in this direction found that we can reduce harmful outputs by fine-tuning on a small curated dataset of human demonstrations.11 Other research has focused on filtering the pre-training dataset,12 safety-specific control tokens,1314 or steering model generations.1516 We are exploring these ideas and others in our ongoing alignment research.
We first evaluate how well outputs from InstructGPT follow user instructions, by having labelers compare its outputs to those from GPT-3. We find that InstructGPT models are significantly preferred on prompts submitted to both the InstructGPT and GPT-3 models on the API. This holds true when we add a prefix to the GPT-3 prompt so that it enters an “instruction-following mode.”