title: Personalized Graph-Based Retrieval for Large Language Models
summary: 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.
paper_authors._id: 677c9728ae2cce31b6aa977b
paper_authors.name: Steven Au
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paper_authors.fullname: Steven Au
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paper_authors.name: Cameron J. Dimacali
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paper_authors.fullname: Cameron Dimacali
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paper_authors.name: Ojasmitha Pedirappagari
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paper_authors.fullname: Ojasmitha Pedirappagari
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paper_authors.user: Ojasmitha17
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paper_authors.name: Namyong Park
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paper_authors.fullname: Namyong Park
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paper_authors.user: namyongp
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paper_authors.name: Franck Dernoncourt
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paper_authors.fullname: Franck Dernoncourt
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paper_authors.user: Franck-Dernoncourt
paper_authors._id: 677c9728ae2cce31b6aa9780
paper_authors.name: Yu Wang
paper_authors._id: 677c9728ae2cce31b6aa9781
paper_authors.name: Nikos Kanakaris
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paper_authors._id: 64c3d7543e30498cca75acaf
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paper_authors.fullname: Nikos Kanakaris
paper_authors.type: user
paper_authors.user: nkanak
paper_authors._id: 677c9728ae2cce31b6aa9782
paper_authors.name: Hanieh Deilamsalehy
paper_authors._id: 677c9728ae2cce31b6aa9783
paper_authors.name: Ryan A. Rossi
paper_authors.status: admin_assigned
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paper_authors._id: 62a3ab83e4dd6252344d27cd
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paper_authors.fullname: Ryan A. Rossi
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paper_authors.user: ryanrossi
paper_authors._id: 677c9728ae2cce31b6aa9784
paper_authors.name: Nesreen K. Ahmed
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paper_authors.fullname: Nesreen Ahmed
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paper_authors.user: nkahmed
paper_title: Personalized Graph-Based Retrieval for Large Language Models
paper_summary: 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.
paper_ai_summary: PGraphRAG uses user-centric knowledge graphs to enhance personalization in text generation, outperforming existing methods especially in scenarios with sparse data.
paper_ai_keywords: large language models personalization context-aware responses user history knowledge graphs retrieval-augmented generation contextual understanding personalized text generation tasks Personalized Graph-based Benchmark