The increasing demand for personalized interactions with large language
models (LLMs) calls for the development of methodologies capable of accurately
and efficiently identifying user opinions and preferences. Retrieval
augmentation emerges as an effective strategy, as it can accommodate a vast
number of users without the costs from fine-tuning. Existing research, however,
has largely focused on enhancing the retrieval stage and devoted limited
exploration toward optimizing the representation of the database, a crucial
aspect for tasks such as personalization. In this work, we examine the problem
from a novel angle, focusing on how data can be better represented for more
efficient retrieval in the context of LLM customization. To tackle this
challenge, we introduce Persona-DB, a simple yet effective framework consisting
of a hierarchical construction process to improve generalization across task
contexts and collaborative refinement to effectively bridge knowledge gaps
among users. In the task of response forecasting, Persona-DB demonstrates
superior efficiency in maintaining accuracy with a significantly reduced
retrieval size, a critical advantage in scenarios with extensive histories or
limited context windows. Our experiments also indicate a marked improvement of
over 15% under cold-start scenarios, when users have extremely sparse data.
Furthermore, our analysis reveals the increasing importance of collaborative
knowledge as the retrieval capacity expands.

通过引入 Persona-DB 框架，本文从一个新的角度考虑数据如何更好地表示，以实现对大型语言模型 (LLMs) 定制过程中更高效的检索，同时在多个实验中展示了 Persona-DB 在任务上下文中的泛化能力和在冷启动情况下的显著改进。

Persona-DB: 高效的大型语言模型个性化应答预测与协同数据精炼

Persona-DB: Efficient Large Language Model Personalization for Response  Prediction with Collaborative Data Refinement

Predicting how a user responds to news events enables important applications
such as allowing intelligent agents or content producers to estimate the effect
on different communities and revise unreleased messages to prevent unexpected
bad outcomes such as social conflict and moral injury. We present a new task,
Response Forecasting on Personas for News Media, to estimate the response a
persona (characterizing an individual or a group) might have upon seeing a news
message. Compared to the previous efforts which only predict generic comments
to news, the proposed task not only introduces personalization in the modeling
but also predicts the sentiment polarity and intensity of each response. This
enables more accurate and comprehensive inference on the mental state of the
persona. Meanwhile, the generated sentiment dimensions make the evaluation and
application more reliable. We create the first benchmark dataset, which
consists of 13,357 responses to 3,847 news headlines from Twitter. We further
evaluate the SOTA neural language models with our dataset. The empirical
results suggest that the included persona attributes are helpful for the
performance of all response dimensions. Our analysis shows that the
best-performing models are capable of predicting responses that are consistent
with the personas, and as a byproduct, the task formulation also enables many
interesting applications in the analysis of social network groups and their
opinions, such as the discovery of extreme opinion groups.

本文提出了一个新任务 ——Response Forecasting on Personas for News Media，并创建了一个包含 13,357 条回复的数据集，以预测不同人物或人群对新闻事件的不同响应，其中任务除了预测评论的情感极性和强度外，还引入了个性化因素，利用最新的神经语言模型进行研究，分析结果表明，加入人物特征有助于预测响应的所有维度，同时该任务的制定还能够应用于社交网络分析中的极端意见群体讨论等多种有趣应用。