As the diversity of users increases, the capability of providing personalized
responses by large language models (LLMs) has become increasingly important.
Existing approaches have only limited successes in LLM personalization, due to
the absence of personalized learning or the reliance on shared personal data.
This paper proposes a new approach for a few-shot personalization of LLMs with
their mis-aligned responses (Fermi). Our key idea is to learn a set of
personalized prompts for each user by progressively improving the prompts using
LLMs, based on user profile (e.g., demographic information) and a few examples
of previous opinions. During an iterative process of prompt improvement, we
incorporate the contexts of mis-aligned responses by LLMs, which are especially
crucial for the effective personalization of LLMs. In addition, we develop an
effective inference method to further leverage the context of the test query
and the personalized prompts. Our experimental results demonstrate that Fermi
significantly improves performance across various benchmarks, compared to the
best-performing baselines.

本研究提出了一种新方法，通过使用大型语言模型（LLMs）的误配响应来进行少量示范个性化，其关键思想是通过改进个性化提示来逐步改善每个用户的提示，特别关注 LLMs 的误配响应的上下文，以及为测试查询和个性化提示开发有效的推理方法。实验结果表明，相比于最佳基准线，Fermi 在各种基准测试中显著提高了性能。

对齐不准确响应的 LLMs 的少样本个性化

Few-shot Personalization of LLMs with Mis-aligned Responses

Recently, methods investigating how to adapt large language models (LLMs) for
specific scenarios have gained great attention. Particularly, the concept of
\textit{persona}, originally adopted in dialogue literature, has re-surged as a
promising avenue. However, the growing research on persona is relatively
disorganized, lacking a systematic overview. To close the gap, we present a
comprehensive survey to categorize the current state of the field. We identify
two lines of research, namely (1) LLM Role-Playing, where personas are assigned
to LLMs, and (2) LLM Personalization, where LLMs take care of user personas. To
the best of our knowledge, we present the first survey tailored for LLM
role-playing and LLM personalization under the unified view of persona,
including taxonomy, current challenges, and potential directions. To foster
future endeavors, we actively maintain a paper collection available to the
community: this https URL

总结：该论文是一份针对大型语言模型中角色扮演和个性化的综述调查，包括对角色扮演和个性化的研究分类、当前挑战和未来方向，并提供了供社区使用的论文资源。

LLMs 中的两种角色形象：角色扮演与个性化的调查

Two Tales of Persona in LLMs: A Survey of Role-Playing and  Personalization

Large language models (LLMs) have demonstrated remarkable proficiency in a
range of natural language processing tasks. Once deployed, LLMs encounter users
with personalized factual knowledge, and such personalized knowledge is
consistently reflected through users' interactions with the LLMs. To enhance
user experience, real-time model personalization is essential, allowing LLMs to
adapt user-specific knowledge based on user feedback during human-LLM
interactions. Existing methods mostly require back-propagation to finetune the
model parameters, which incurs high computational and memory costs. In
addition, these methods suffer from low interpretability, which will cause
unforeseen impacts on model performance during long-term use, where the user's
personalized knowledge is accumulated extensively.To address these challenges,
we propose Knowledge Graph Tuning (KGT), a novel approach that leverages
knowledge graphs (KGs) to personalize LLMs. KGT extracts personalized factual
knowledge triples from users' queries and feedback and optimizes KGs without
modifying the LLM parameters. Our method improves computational and memory
efficiency by avoiding back-propagation and ensures interpretability by making
the KG adjustments comprehensible to humans.Experiments with state-of-the-art
LLMs, including GPT-2, Llama2, and Llama3, show that KGT significantly improves
personalization performance while reducing latency and GPU memory costs.
Ultimately, KGT offers a promising solution of effective, efficient, and
interpretable real-time LLM personalization during user interactions with the
LLMs.

通过使用知识图谱进行实时个性化，文章提出了一种提高大型语言模型用户体验的方法，而不需要修改模型参数，从而改善了计算和内存效率，并保证解释性和个性化性能。

知识图谱调优：基于人类反馈的实时大型语言模型个性化

Knowledge Graph Tuning: Real-time Large Language Model Personalization  based on Human Feedback

Large Language Models (LLMs) have exhibited remarkable proficiency in
comprehending and generating natural language. On the other hand, personalized
LLM response generation holds the potential to offer substantial benefits for
individuals in critical areas such as medical. Existing research has explored
memory-augmented methods to prompt the LLM with pre-stored user-specific
knowledge for personalized response generation in terms of new queries. We
contend that such paradigm is unable to perceive fine-granularity information.
In this study, we propose a novel \textbf{M}emory-\textbf{i}njected approach
using parameter-efficient fine-tuning (PEFT) and along with a Bayesian
Optimisation searching strategy to achieve \textbf{L}LM
\textbf{P}ersonalization(\textbf{MiLP}).

本研究提出了一种使用参数高效微调和贝叶斯优化搜索策略的记忆注入方法来实现大型语言模型的个性化响应生成 (MiLP)。