大型语言模型的个性化基于图的检索

Personalized Graph-Based Retrieval for Large Language Models

January 4, 2025

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

cs.AI

摘要

随着大型语言模型（LLMs）的发展，它们提供个性化和上下文感知响应的能力，为改善用户体验带来了变革性潜力。然而，现有的个性化方法通常仅依赖用户历史来增强提示，从而限制了它们在生成定制输出方面的有效性，尤其是在数据稀疏的冷启动场景中。为了解决这些局限性，我们提出了基于个性化图检索增强生成（PGraphRAG）的框架，利用以用户为中心的知识图来丰富个性化。通过直接将结构化用户知识整合到检索过程中，并用用户相关上下文增强提示，PGraphRAG增强了上下文理解和输出质量。我们还介绍了基于个性化图的文本生成基准，旨在评估在用户历史稀疏或不可用的实际环境中的个性化文本生成任务。实验结果表明，PGraphRAG在各种任务中明显优于最先进的个性化方法，展示了基于图检索的个性化的独特优势。

English

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.