Personalized Graph-Based Retrieval for Large Language Models
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.
677c9728ae2cce31b6aa977b
Steven Au
admin_assigned
2025-01-07T09:48:43.716Z
65dd26cf5012ec503f0137d1
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/jPhOKOrd9axHhUfa7SFic.jpeg
Steven Au
user
StevenAu
677c9728ae2cce31b6aa977c
Cameron J. Dimacali
admin_assigned
2025-01-07T09:49:00.825Z
63888721f67ad3caa9d2701d
https://cdn-avatars.huggingface.co/v1/production/uploads/1669891869519-noauth.png
Cameron Dimacali
user
Tobilee
677c9728ae2cce31b6aa977d
Ojasmitha Pedirappagari
admin_assigned
2025-01-07T09:49:08.187Z
6561171fe0a7720b6ae89483
/avatars/34e3b8f41084811b9a5f8022b698f3a9.svg
Ojasmitha Pedirappagari
user
Ojasmitha17
677c9728ae2cce31b6aa977e
Namyong Park
admin_assigned
2025-01-07T09:49:14.438Z
65f9ecb65ff1f80a69598e33
/avatars/e0dd617533d9d7d6167a8dc4383ae08e.svg
Namyong Park
user
namyongp
677c9728ae2cce31b6aa977f
Franck Dernoncourt
claimed_verified
2025-01-07T08:42:05.220Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
677c9728ae2cce31b6aa9780
Yu Wang
677c9728ae2cce31b6aa9781
Nikos Kanakaris
claimed_verified
2025-01-07T08:42:01.570Z
64c3d7543e30498cca75acaf
/avatars/9608497e2b4d0a57cff69843fd09a077.svg
Nikos Kanakaris
user
nkanak
677c9728ae2cce31b6aa9782
Hanieh Deilamsalehy
677c9728ae2cce31b6aa9783
Ryan A. Rossi
admin_assigned
2025-01-07T09:49:50.928Z
62a3ab83e4dd6252344d27cd
/avatars/7ca8510f70a58dc207b104240e30c35c.svg
Ryan A. Rossi
user
ryanrossi
677c9728ae2cce31b6aa9784
Nesreen K. Ahmed
claimed_verified
2025-01-07T08:42:03.376Z
663e588ccca86c8371a913d9
https://cdn-avatars.huggingface.co/v1/production/uploads/663e588ccca86c8371a913d9/wgVGw5U77cqwtjovhsXyR.jpeg
Nesreen Ahmed
user
nkahmed
Personalized Graph-Based Retrieval for Large Language Models
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.
PGraphRAG uses user-centric knowledge graphs to enhance personalization in text generation, outperforming existing methods especially in scenarios with sparse data.
large language models personalization context-aware responses user history knowledge graphs retrieval-augmented generation contextual understanding personalized text generation tasks Personalized Graph-based Benchmark