Teach LLMs to Personalize -- An Approach inspired by Writing Education
Cheng Li Mingyang Zhang Qiaozhu Mei Yaqing Wang Spurthi Amba Hombaiah Yi Liang Michael Bendersky
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.