  Global models are trained to be as generalizable as possible, with user
invariance considered desirable since the models are shared across multitudes
of users. As such, these models are often unable to produce personalized
responses for individual users, based on their data. Contrary to widely-used
personalization techniques based on few-shot learning, we propose
UserIdentifier, a novel scheme for training a single shared model for all
users. Our approach produces personalized responses by adding fixed,
non-trainable user identifiers to the input data. We empirically demonstrate
that this proposed method outperforms the prefix-tuning based state-of-the-art
approach by up to 13%, on a suite of sentiment analysis datasets. We also show
that, unlike prior work, this method needs neither any additional model
parameters nor any extra rounds of few-shot fine-tuning.
