  This paper highlights the importance of personalization in large language
models and introduces the LaMP benchmark -- a novel benchmark for training and
evaluating language models for producing personalized outputs. LaMP offers a
comprehensive evaluation framework with diverse language tasks and multiple
entries for each user profile. It consists of seven personalized tasks,
spanning three text classification and four text generation tasks. We
additionally propose two retrieval augmentation approaches that retrieve
personal items from each user profile for personalizing language model outputs.
To this aim, we study various retrieval models, including term matching,
semantic matching, and time-aware methods. Extensive experiments on LaMP for
zero-shot and fine-tuned language models demonstrate the efficacy of the
proposed retrieval augmentation approach and highlight the impact of
personalization in various natural language tasks.
