It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, just as Application Programming Interfaces (APIs) control the behavior of programs by altering hyperparameters. In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained model to produce desired outputs. Importantly, no permanent changes are made to the weights of the original model, allowing us to re-purpose pretrained models for new tasks without overwriting any aspect of the language model. We also contribute a new data set construction algorithm and GAN-inspired loss function that allows us to train NPI models to control outputs of autoregressive transformers. In experiments against other state-of-the-art approaches, we demonstrate the efficacy of our methods using OpenAI's GPT-2 model, successfully controlling noun selection, topic aversion, offensive speech filtering, and other aspects of language while largely maintaining the controlled model's fluency under deterministic settings.

Every day, our kids are swept through the world by algorithms. YouTube algorithms decide what videos they watch, GPS algorithms map what route they take to school, Spotify algorithms select what songs they hear, and personal assistants like Siri and Alexa advise them -- all of it driven by artificial intelligence. Kids (and adults!) leave these passive engagements with AI without any physical product -- just an endless stream of passive consumption. Instead of getting carried away by these digital currents, teachers, parents, and caregivers should show kids how to experiment with powerful tools like machine learning and neural networks. We must raise kids who are capable of working alongside artificial intelligence in the workplace.