Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models

• URL: http://arxiv.org/abs/2505.17051v1
• Date: Fri, 16 May 2025 13:34:25 GMT
• Title: Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models
• Authors: Bernd Huber, Ghazal Fazelnia, Andreas Damianou, Sebastian Peleato, Max Lefarov, Praveen Ravichandran, Marco De Nadai, Mounia Lalmas-Roellke, Paul N. Bennett,
• Abstract summary: Large language models (LLMs) excel at generating contextually relevant content.<n>We propose Embedding-to-Prefix (E2P), a parameter-efficient method that injects context embeddings into an LLM's hidden representation space.<n>We evaluate E2P across two public datasets and in a production setting: dialogue personalization on Persona-Chat, contextual headline generation on PENS, and large-scale personalization for music and podcast consumption.
• Score: 6.445337954429245
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) excel at generating contextually relevant content. However, tailoring these outputs to individual users for effective personalization is a significant challenge. While rich user-specific information often exists as pre-existing user representations, such as embeddings learned from preferences or behaviors, current methods to leverage these for LLM personalization typically require costly fine-tuning or token-heavy prompting. We propose Embedding-to-Prefix (E2P), a parameter-efficient method that injects pre-computed context embeddings into an LLM's hidden representation space through a learned projection to a single soft token prefix. This enables effective personalization while keeping the backbone model frozen and avoiding expensive adaptation techniques. We evaluate E2P across two public datasets and in a production setting: dialogue personalization on Persona-Chat, contextual headline generation on PENS, and large-scale personalization for music and podcast consumption. Results show that E2P preserves contextual signals and achieves strong performance with minimal computational overhead, offering a scalable, efficient solution for contextualizing generative AI systems.

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