  Language models are transforming the ways that their users engage with the
world. Despite impressive capabilities, over-consumption of language model
outputs risks propagating unchecked errors in the short-term and damaging human
capabilities for critical thinking in the long-term, particularly in
knowledge-based tasks. How can we develop scaffolding around language models to
curate more appropriate use? We propose selective frictions for language model
experiences, inspired by behavioral science interventions, to dampen misuse.
Frictions involve small modifications to a user's experience, e.g., the
addition of a button impeding model access and reminding a user of their
expertise relative to the model. Through a user study with real humans, we
observe shifts in user behavior from the imposition of a friction over LLMs in
the context of a multi-topic question-answering task as a representative task
that people may use LLMs for, e.g., in education and information retrieval. We
find that frictions modulate over-reliance by driving down users' click rates
while minimally affecting accuracy for those topics. Yet, frictions may have
unintended effects. We find marked differences in users' click behaviors even
on topics where frictions were not provisioned. Our contributions motivate
further study of human-AI behavioral interaction to inform more effective and
appropriate LLM use.
