title: Teach LLMs to Personalize -- An Approach inspired by Writing Education
summary: 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.
paper_authors._id: 64ddafb95f4723b556aa8f48
paper_authors.name: Cheng Li
paper_authors._id: 64ddafb95f4723b556aa8f49
paper_authors.name: Mingyang Zhang
paper_authors._id: 64ddafb95f4723b556aa8f4a
paper_authors.name: Qiaozhu Mei
paper_authors._id: 64ddafb95f4723b556aa8f4b
paper_authors.name: Yaqing Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-08-17T14:37:22.383Z
paper_authors._id: 62cb0f38b189b6164d2a7900
paper_authors.avatarUrl: /avatars/8bdf9705822fba3518e25db45d894535.svg
paper_authors.fullname: Yaqing Wang
paper_authors.type: user
paper_authors.user: yaqingwang90
paper_authors._id: 64ddafb95f4723b556aa8f4c
paper_authors.name: Spurthi Amba Hombaiah
paper_authors._id: 64ddafb95f4723b556aa8f4d
paper_authors.name: Yi Liang
paper_authors._id: 64ddafb95f4723b556aa8f4e
paper_authors.name: Michael Bendersky
paper_title: Teach LLMs to Personalize -- An Approach inspired by Writing Education
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) multistage framework multitask framework retrieval ranking summarization synthesis generation multitask setting reading proficiency writing ability