PGraphRAG

As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. We propose Personalized Graph-based Retrieval-Augmented Generation (PGraphRAG), a framework and benchmark 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. This benchmark is designed to evaluate personalized text generation tasks in real-world settings where user history is sparse or unavailable.

If you use the PGraphRAG benchmark in your work, please cite our paper: Personalized Graph-Based Retrieval for Large Language Models.

@article{pgraphrag, title={Personalized Graph-Based Retrieval for Large Language Models}, author={S. Au, C.J. Dimacali, O. Pedirappagari, N. Park, F. Dernoncourt, Y. Wang, N. Kanakaris, H. Deilamsalehy, R.A. Rossi, N.K. Ahmed}, year={2025}, eprint={2501.02157}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.02157} }