Personalization has emerged as a critical research area in modern intelligent
systems, focusing on mining users' behavioral history and adapting to their
preferences for delivering tailored experiences. Despite the remarkable
few-shot capabilities exhibited by black-box large language models (LLMs), the
inherent opacity of their model parameters presents significant challenges in
aligning the generated output with individual expectations. Existing solutions
have primarily focused on prompt design to incorporate user-specific profiles
and behaviors; however, such approaches often struggle to generalize
effectively due to their inability to capture shared knowledge among all users.
To address these challenges, we propose HYDRA, a model factorization framework
that captures both user-specific behavior patterns from historical data and
shared general knowledge among all users to deliver personalized generation. In
order to capture user-specific behavior patterns, we first train a reranker to
prioritize the most useful information from top-retrieved relevant historical
records. By combining the prioritized history with the corresponding query, we
train an adapter to align the output with individual user-specific preferences,
eliminating the reliance on access to inherent model parameters of black-box
LLMs. Both the reranker and the adapter can be decomposed into a base model
with multiple user-specific heads, resembling a hydra. The base model maintains
shared knowledge across users, while the multiple personal heads capture
user-specific preferences. Experimental results demonstrate that HYDRA
outperforms existing state-of-the-art prompt-based methods by an average
relative improvement of 9.01% across five diverse personalization tasks in the
LaMP benchmark. Our implementation is available at
this https URL

通过模型分解与知识捕捉，HYDRA 提供了一种个性化生成的解决方案，实现了用户特定行为模式与共享知识的结合。在 LaMP 基准测试中，HYDRA 相对于现有的基于提示的方法平均相对改进 9.01％。

HYDRA: 黑盒 LLM 个性化的模型分解框架

HYDRA: Model Factorization Framework for Black-Box LLM Personalization

Highlight detection models are typically trained to identify cues that make
visual content appealing or interesting for the general public, with the
objective of reducing a video to such moments. However, the "interestingness"
of a video segment or image is subjective. Thus, such highlight models provide
results of limited relevance for the individual user. On the other hand,
training one model per user is inefficient and requires large amounts of
personal information which is typically not available. To overcome these
limitations, we present a global ranking model which conditions on each
particular user's interests. Rather than training one model per user, our model
is personalized via its inputs, which allows it to effectively adapt its
predictions, given only a few user-specific examples. To train this model, we
create a large-scale dataset of users and the GIFs they created, giving us an
accurate indication of their interests. Our experiments show that using the
user history substantially improves the prediction accuracy. On our test set of
850 videos, our model improves the recall by 8% with respect to generic
highlight detectors. Furthermore, our method proves more precise than the
user-agnostic baselines even with just one person-specific example.

通过全球排名模型，我们使用用户历史创建了大规模的数据集，为用户兴趣打分实现了个性化模型，大大提高了通用高光侦测器的回归率并比单一用户不可知的基线更加精确。