Title: Integrating LLMs with Persona-Plug for Enhanced Personalization

Original: arXiv:2409.11901v2 Announce Type: replace Abstract: Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. Some of them involve fine-tuning a unique personalized LLM for each user, which is too expensive for widespread application. Alternative approaches introduce personalization information in a plug-and-play manner by retrieving the user's relevant historical texts as demonstrations. However, this retrieval-based strategy may break the continuity of the user history and fail to capture the user's overall styles and patterns, hence leading to sub-optimal performance. To address these challenges, we propose a novel personalized LLM model, PPlug. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module. By attaching this embedding to the task input, LLMs can better understand and capture user habits and preferences, thereby producing more personalized outputs without tuning their own parameters. Extensive experiments on various tasks in the language model personalization (LaMP) benchmark demonstrate that the proposed model significantly outperforms existing personalized LLM approaches.

Rewrite:

Personalization is essential across a wide range of language applications, as individuals often seek distinct responses that reflect their unique interests, even when facing identical prompts. Consequently, researchers have developed methods to tailor Large Language Models (LLMs) to align with specific user preferences. One common strategy involves fine-tuning a distinct LLM for every single user; however, the high cost associated with this approach hinders its scalability for broad deployment.

To overcome these limitations, other methods employ a plug-and-play mechanism that retrieves relevant historical texts from a user’s past interactions to serve as examples. While this avoids the need for fine-tuning, it has notable drawbacks. Retrieval-based techniques can disrupt the chronological flow of a user’s history and often miss broader stylistic nuances and long-term patterns, resulting in less effective performance.

In this work, we introduce PPlug, a new framework designed to enhance LLM personalization. PPlug utilizes a lightweight, plug-in module dedicated to user embedding. This module analyzes a user’s entire historical context to generate a specific embedding for each individual. When this embedding is appended to the task input, the LLM gains a deeper understanding of the user’s habits and preferences. This allows the model to generate highly personalized outputs without requiring any adjustments to its internal parameters. Our extensive testing on multiple tasks within the Language Model Personalization (LaMP) benchmark reveals that PPlug substantially outperforms current state-of-the-art personalized LLM techniques.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC