Personalized text generation is an emerging research area that has attracted
much attention in recent years. Most studies in this direction focus on a
particular domain by designing bespoke features or models. In this work, we
propose a general approach for personalized text generation using large
language models (LLMs). Inspired by the practice of writing education, we
develop a multistage and multitask framework to teach LLMs for personalized
generation. In writing instruction, the task of writing from sources is often
decomposed into multiple steps that involve finding, evaluating, summarizing,
synthesizing, and integrating information. Analogously, our approach to
personalized text generation consists of multiple stages: retrieval, ranking,
summarization, synthesis, and generation. In addition, we introduce a multitask
setting that helps the model improve its generation ability further, which is
inspired by the observation in education that a student's reading proficiency
and writing ability are often correlated. We evaluate our approach on three
public datasets, each of which covers a different and representative domain.
Our results show significant improvements over a variety of baselines.

个性化文本生成是一个新兴的研究领域，通过使用大型语言模型 (LLMs)，我们提出了一种通用的个性化文本生成方法。借鉴写作教育的实践，我们开发了一个多阶段和多任务的框架来教授 LLMs 进行个性化生成。我们的方法包括检索、排名、总结、综合和生成多个阶段，并引入了一个多任务设置来进一步提高模型的生成能力。我们在三个涵盖不同代表性领域的公开数据集上评估了我们的方法，结果显示相对于各种基线模型，我们取得了显著的改进。