面向大语言模型个性化的潜在用户间差异建模
Latent Inter-User Difference Modeling for LLM Personalization
AI总结：
本文提出DEP框架，通过在潜在空间对比用户与相似同侪的嵌入来建模用户间差异，并用稀疏自编码器筛选任务相关特征注入冻结LLM，在个性化评论生成中优于基线方法。
摘要：
大语言模型（LLMs）正日益融入用户的日常生活，导致对个性化输出的需求不断增长。以往的工作侧重于利用用户自身的历史记录，忽视了对于有效个性化至关重要的用户间差异。尽管近期的工作尝试对这种差异进行建模，但对基于语言的提示的依赖往往阻碍了有意义区别的有效提取。为了解决这些问题，我们提出了基于差异感知嵌入的个性化框架（DEP），该框架在潜在空间中建模用户间差异，而不是依赖语言提示。DEP通过将用户的嵌入与那些接触过相似内容的同侪的嵌入进行对比来构建软提示，从而突出相对行为信号。随后，稀疏自编码器对用户特定嵌入和差异感知嵌入进行过滤和压缩，仅保留与任务相关的特征，再将其注入冻结的大语言模型。在个性化评论生成上的实验表明，DEP在多个指标上持续优于基线方法。我们的代码可在 https://github.com/SnowCharmQ/DEP 获取。
英文摘要：
Large language models (LLMs) are increasingly integrated into users' daily lives, leading to a growing demand for personalized outputs. Previous work focuses on leveraging a user's own history, overlooking inter-user differences that are crucial for effective personalization. While recent work has attempted to model such differences, the reliance on language-based prompts often hampers the effective extraction of meaningful distinctions. To address these issues, we propose Difference-aware Embedding-based Personalization (DEP), a framework that models inter-user differences in the latent space instead of relying on language prompts. DEP constructs soft prompts by contrasting a user's embedding with those of peers who engaged with similar content, highlighting relative behavioral signals. A sparse autoencoder then filters and compresses both user-specific and difference-aware embeddings, preserving only task-relevant features before injecting them into a frozen LLM. Experiments on personalized review generation show that DEP consistently outperforms baseline methods across multiple metrics. Our code is available at https://github.com/SnowCharmQ/DEP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
