  Large language models (LLM) excel at a variety of natural language processing
tasks, yet they struggle to generate personalized content for individuals,
particularly in real-world scenarios like scientific writing. Addressing this
challenge, we introduce STEP-BACK PROFILING to personalize LLMs by distilling
user history into concise profiles, including essential traits and preferences
of users. To conduct the experiments, we construct a Personalized Scientific
Writing (PSW) dataset to study multi-user personalization. PSW requires the
models to write scientific papers given specialized author groups with diverse
academic backgrounds. As for the results, we demonstrate the effectiveness of
capturing user characteristics via STEP-BACK PROFILING for collaborative
writing. Moreover, our approach outperforms the baselines by up to 3.6 points
on the general personalization benchmark (LaMP), including 7 personalization
LLM tasks. Our ablation studies validate the contributions of different
components in our method and provide insights into our task definition. Our
dataset and code are available at
\url{https://github.com/gersteinlab/step-back-profiling}.
