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

TL;DR

Embedding-to-Prefix (E2P) introduces a parameter-efficient approach to personalize large language models by injecting user embeddings into the model's hidden states, avoiding costly fine-tuning and maintaining high performance.

Contribution

E2P is a novel method that efficiently personalizes LLMs using pre-computed user embeddings through a learned projection, without modifying the backbone model.

Findings

E2P achieves strong personalization performance across multiple datasets.

E2P maintains contextual signals with minimal computational overhead.

E2P offers a scalable solution for large-scale personalization in generative AI.

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.