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Global models are trained to be as generalizable as possible, with userinvariance considered desirable since the models are shared across multitudesof users. As such, these models are often unable to produce personalizedresponses for individual users, based on their data. Contrary to widely-usedpersonalization techniques based on few-shot learning, we proposeUserIdentifier, a novel scheme for training a single shared model for allusers. Our approach produces personalized responses by adding fixed,non-trainable user identifiers to the input data. We empirically demonstratethat this proposed method outperforms the prefix-tuning based state-of-the-artapproach by up to 13%, on a suite of sentiment analysis datasets. We also showthat, unlike prior work, this method needs neither any additional modelparameters nor any extra rounds of few-shot fine-tuning.

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis