# Personalized Graph-Based Retrieval for Large Language Models

Source: [Personalized Graph-Based Retrieval for Large Language Models](<https://www.sota2.com/research/paper/68fa79081e209131cea7bcb1>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T04:52:00.639Z

Authors: Steven Au, Cameron J. Dimacali, Ojasmitha Pedirappagari, Namyong Park, Franck Dernoncourt, Yu Wang, Nikos Kanakaris, Hanieh Deilamsalehy, Ryan A. Rossi, Nesreen K. Ahmed

Published: 2025-01-04T01:46:49.000Z

## Abstract

As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. Existing personalization approaches, however, often rely solely on user history to augment the prompt, limiting their effectiveness in generating tailored outputs, especially in cold-start scenarios with sparse data. To address these limitations, we propose Personalized Graph-based Retrieval-Augmented Generation (PGraphRAG), a framework that leverages user-centric knowledge graphs to enrich personalization. By directly integrating structured user knowledge into the retrieval process and augmenting prompts with user-relevant context, PGraphRAG enhances contextual understanding and output quality. We also introduce the Personalized Graph-based Benchmark for Text Generation, designed to evaluate personalized text generation tasks in real-world settings where user history is sparse or unavailable. Experimental results show that PGraphRAG significantly outperforms state-of-the-art personalization methods across diverse tasks, demonstrating the unique advantages of graph-based retrieval for personalization.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Long Text Generation on Hotel Experience](<https://www.sota2.com/research/sota/long-text-generation-on-hotel-experience.md>): Long Text Generation; Hotel Experience; ROUGE-1: 0.26.

- [Long Text Generation on Personalization Benchmark Long Text](<https://www.sota2.com/research/sota/long-text-generation-on-personalization-benchmark-long-text.md>): Long Text Generation; Personalization Benchmark Long Text; R-1 Score: 0.213.

- [Long Text Generation on Stylized Feedback Generation Benchmark](<https://www.sota2.com/research/sota/long-text-generation-on-stylized-feedback-generation-benchmark.md>): Long Text Generation; Stylized Feedback Generation Benchmark; R-1: 21.3.

- [Long Text Generation on Amazon Review](<https://www.sota2.com/research/sota/long-text-generation-on-amazon-review.md>): Long Text Generation; Amazon Review; ROUGE-1: 18.3.

- [Short Text Generation on Personalization Benchmark Short Text](<https://www.sota2.com/research/sota/short-text-generation-on-personalization-benchmark-short-text.md>): Short-text generation; Personalization Benchmark Short Text; R-1: 0.126.

- [Short Text Generation on Hotel Experience](<https://www.sota2.com/research/sota/short-text-generation-on-hotel-experience.md>): Short-text generation; Hotel Experience; ROUGE-1: 12.5.

- [Short Text Generation on Stylized Feedback Generation Benchmark](<https://www.sota2.com/research/sota/short-text-generation-on-stylized-feedback-generation-benchmark.md>): Short-text generation; Stylized Feedback Generation Benchmark; ROUGE-1 Score: 0.139.

- [Short Text Generation on Amazon Review](<https://www.sota2.com/research/sota/short-text-generation-on-amazon-review.md>): Short-text generation; Amazon Review; ROUGE-1: 12.4.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.02157>)

[PDF](<https://arxiv.org/pdf/2501.02157.pdf>)