Large language models (LLMs) 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. Regarding our experiments, we construct a Personalized Scientific Writing (PSW) dataset to study multiuser 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 extensive 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}.

使用Step-Back Profiling方法将大型语言模型个性化，以便在科学写作等实际场景中生成个性化内容。实验结果表明，我们的方法在个性化基准(LaMP)上相对于基准方法提高了3.6个点，包括7个个性化大型语言模型任务。

Step-Back Profiling: 提取用户历史信息以个性化科学写作