# Personalized Graph-Based Retrieval for Large Language Models

arXiv: https://arxiv.org/abs/2501.02157 · HTML: https://mlsanity.com/paper/2501.02157/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 60 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering](https://mlsanity.com/paper/2007.01282/) (2020)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [LaMP: When Large Language Models Meet Personalization](https://mlsanity.com/paper/2304.11406/) (2023)
- [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://mlsanity.com/paper/1906.00091/) (2019)
- [The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics](https://mlsanity.com/paper/2102.01672/) (2021)
- [Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders](https://mlsanity.com/paper/2403.03952/) (2024)
- [Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation](https://mlsanity.com/paper/2404.05970/) (2024)
- [LongLaMP: A Benchmark for Personalized Long-form Text Generation](https://mlsanity.com/paper/2407.11016/) (2024)
- [Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generation](https://mlsanity.com/paper/2305.18846/) (2023)
- [A Decade of Knowledge Graphs in Natural Language Processing: A Survey](https://mlsanity.com/paper/2210.00105/) (2022)
- [Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training](https://mlsanity.com/paper/2306.03166/) (2023)
- [Personalized Text Generation with Fine-Grained Linguistic Control](https://mlsanity.com/paper/2402.04914/) (2024)
- [Large Language Models Meet Knowledge Graphs to Answer Factoid Questions](https://mlsanity.com/paper/2310.02166/) (2023)
- [Cold-Start Aware User and Product Attention for Sentiment Classification](https://mlsanity.com/paper/1806.05507/) (2018)
- [Noisy Pairing and Partial Supervision for Stylized Opinion Summarization](https://mlsanity.com/paper/2211.08723/) (2022)

## Built on by (most cited first, 10 of 10)

- [A Survey of Personalized Large Language Models: Progress and Future Directions](https://mlsanity.com/paper/2502.11528/) (2025)
- [A Survey of Personalization: From RAG to Agent](https://mlsanity.com/paper/2504.10147/) (2025)
- [A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations](https://mlsanity.com/paper/2505.14106/) (2025)
- [EgoMem: Lifelong Memory Agent for Full-duplex Omnimodal Models](https://mlsanity.com/paper/2509.11914/) (2025)
- [Rethinking Personalization in Large Language Models at the Token Level](https://mlsanity.com/paper/2603.06595/) (2026)
- [Reasoning-Based Personalized Generation for Users with Sparse Data](https://mlsanity.com/paper/2602.21219/) (2026)
- [PeReGrINE: Evaluating Personalized Review Fidelity with User Item Graph Context](https://mlsanity.com/paper/2604.07788/) (2026)
- [Matching Game Preferences Through Dialogical Large Language Models: A Perspective](https://mlsanity.com/paper/2507.20000/) (2025)
- [Sparse Personalized Text Generation with Multi-Trajectory Reasoning](https://mlsanity.com/paper/2604.24996/) (2026)
- [Personalized Image Generation with Reasoning and Reflection](https://mlsanity.com/paper/2610.00737/) (2026)

## Abstract (arXiv)

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.