Knowledge Graph Tuning: Real-time Large Language Model Personalization based on Human Feedback

TL;DR

This paper introduces Knowledge Graph Tuning (KGT), a novel method for real-time personalization of large language models using knowledge graphs, which enhances user-specific knowledge adaptation efficiently and interpretably without retraining the models.

Contribution

KGT leverages knowledge graphs to personalize LLMs during interactions, avoiding costly back-propagation and improving interpretability and efficiency.

Findings

KGT improves personalization performance across multiple LLMs.

KGT reduces latency and GPU memory usage during personalization.

KGT maintains model performance without modifying LLM parameters.

Abstract

Large language models (LLMs) have demonstrated remarkable proficiency in a range of natural language processing tasks. Once deployed, LLMs encounter users with personalized factual knowledge, and such personalized knowledge is consistently reflected through users' interactions with the LLMs. To enhance user experience, real-time model personalization is essential, allowing LLMs to adapt user-specific knowledge based on user feedback during human-LLM interactions. Existing methods mostly require back-propagation to finetune the model parameters, which incurs high computational and memory costs. In addition, these methods suffer from low interpretability, which will cause unforeseen impacts on model performance during long-term use, where the user's personalized knowledge is accumulated extensively.To address these challenges, we propose Knowledge Graph Tuning (KGT), a novel approach that leverages knowledge graphs (KGs) to personalize LLMs. KGT extracts personalized factual knowledge triples from users' queries and feedback and optimizes KGs without modifying the LLM parameters. Our method improves computational and memory efficiency by avoiding back-propagation and ensures interpretability by making the KG adjustments comprehensible to humans.Experiments with state-of-the-art LLMs, including GPT-2, Llama2, and Llama3, show that KGT significantly improves personalization performance while reducing latency and GPU memory costs. Ultimately, KGT offers a promising solution of effective, efficient, and interpretable real-time LLM personalization during user interactions with the LLMs.