Teach LLMs to Personalize -- An Approach inspired by Writing Education
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.
64ddafb95f4723b556aa8f48
Cheng Li
64ddafb95f4723b556aa8f49
Mingyang Zhang
64ddafb95f4723b556aa8f4a
Qiaozhu Mei
64ddafb95f4723b556aa8f4b
Yaqing Wang
admin_assigned
2023-08-17T14:37:22.383Z
62cb0f38b189b6164d2a7900
/avatars/8bdf9705822fba3518e25db45d894535.svg
Yaqing Wang
user
yaqingwang90
64ddafb95f4723b556aa8f4c
Spurthi Amba Hombaiah
64ddafb95f4723b556aa8f4d
Yi Liang
64ddafb95f4723b556aa8f4e
Michael Bendersky
Teach LLMs to Personalize -- An Approach inspired by Writing Education
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.
A multistage and multitask framework using large language models enhances personalized text generation by improving retrieval, ranking, summarization, synthesis, and generation skills.
large language models LLMs multistage framework multitask framework retrieval ranking summarization synthesis generation personalized text generation