LLMs + Persona-Plug = Personalized LLMs
Personalization plays a critical role in numerous language tasks and
applications, since users with the same requirements may prefer diverse outputs
based on their individual interests. This has led to the development of various
personalized approaches aimed at adapting large language models (LLMs) to
generate customized outputs aligned with user preferences. Some of them involve
fine-tuning a unique personalized LLM for each user, which is too expensive for
widespread application. Alternative approaches introduce personalization
information in a plug-and-play manner by retrieving the user's relevant
historical texts as demonstrations. However, this retrieval-based strategy may
break the continuity of the user history and fail to capture the user's overall
styles and patterns, hence leading to sub-optimal performance. To address these
challenges, we propose a novel personalized LLM model, . It constructs a
user-specific embedding for each individual by modeling all her historical
contexts through a lightweight plug-in user embedder module. By attaching this
embedding to the task input, LLMs can better understand and capture user habits
and preferences, thereby producing more personalized outputs without tuning
their own parameters. Extensive experiments on various tasks in the language
model personalization (LaMP) benchmark demonstrate that the proposed model
significantly outperforms existing personalized LLM approaches.
66eb8c29d57b0fc0c43521dd
Jiongnan Liu
admin_assigned
2024-09-19T08:20:45.400Z
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jiongnan liu
user
liujiongnan
66eb8c29d57b0fc0c43521de
Yutao Zhu
admin_assigned
2024-09-19T08:20:51.799Z
625e62452a7279d3c77b5c38
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Yutao Zhu
user
yutaozhu94
66eb8c29d57b0fc0c43521df
Shuting Wang
admin_assigned
2024-09-19T08:21:02.553Z
656d68699dcedd16d5596d26
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Shu-Ting Wang
user
stingw
66eb8c29d57b0fc0c43521e0
Xiaochi Wei
66eb8c29d57b0fc0c43521e1
Erxue Min
66eb8c29d57b0fc0c43521e2
Yu Lu
66eb8c29d57b0fc0c43521e3
Shuaiqiang Wang
66eb8c29d57b0fc0c43521e4
Dawei Yin
66eb8c29d57b0fc0c43521e5
Zhicheng Dou
claimed_verified
2024-09-23T06:58:33.624Z
66f0bf59e9d50ec57febf751
/avatars/be97941e60064e5dd806c6fe9db3c537.svg
Zhicheng Dou
user
douzc
LLMs + Persona-Plug = Personalized LLMs
Personalization plays a critical role in numerous language tasks and
applications, since users with the same requirements may prefer diverse outputs
based on their individual interests. This has led to the development of various
personalized approaches aimed at adapting large language models (LLMs) to
generate customized outputs aligned with user preferences. Some of them involve
fine-tuning a unique personalized LLM for each user, which is too expensive for
widespread application. Alternative approaches introduce personalization
information in a plug-and-play manner by retrieving the user's relevant
historical texts as demonstrations. However, this retrieval-based strategy may
break the continuity of the user history and fail to capture the user's overall
styles and patterns, hence leading to sub-optimal performance. To address these
challenges, we propose a novel personalized LLM model, . It constructs a
user-specific embedding for each individual by modeling all her historical
contexts through a lightweight plug-in user embedder module. By attaching this
embedding to the task input, LLMs can better understand and capture user habits
and preferences, thereby producing more personalized outputs without tuning
their own parameters. Extensive experiments on various tasks in the language
model personalization (LaMP) benchmark demonstrate that the proposed model
significantly outperforms existing personalized LLM approaches.
A novel model constructs user-specific embeddings using a lightweight plug-in module to personalize LLM outputs without fine-tuning, improving performance on various tasks.
large language models (LLMs) personalized LLM user-specific embedding plug-in user embedder module language model personalization (LaMP) benchmark