Latent Inter-User Difference Modeling for LLM Personalization

ACL ARR 2025 May Submission8015 Authors

Abstract:

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 \ours consistently outperforms baseline methods across multiple metrics.
Our code is available on an \href{https://anonymous.4open.science/r/DEP-A8A0}{Anonymous GitHub}.

Paper Type: Long

Research Area: Generation

Research Area Keywords: LLM Personalization, Text Generation

Languages Studied: English

Submission Number: 8015