面向大型语言模型的个性化基于图的检索
Personalized Graph-Based Retrieval for Large Language Models
AI总结：
针对现有LLM个性化方法在冷启动场景下依赖用户历史导致效果受限的问题，提出PGraphRAG框架，通过集成以用户为中心的知识图谱增强检索与提示，并构建新基准，显著提升了个性化文本生成质量。
摘要：
随着大型语言模型（LLMs）演进，其提供个性化与情境感知响应的能力为改善用户体验带来了变革性潜力。然而，现有个性化方法往往仅依赖用户历史来增强提示，限制了生成定制输出的有效性，特别是在数据稀疏的冷启动场景中。为解决这些局限，我们提出了个性化基于图的检索增强生成（PGraphRAG），该框架利用以用户为中心的知识图谱来丰富个性化。通过将结构化用户知识直接集成到检索过程中，并用与用户相关的上下文增强提示，PGraphRAG提升了情境理解与输出质量。我们还引入了面向文本生成的个性化基于图基准，旨在评估用户历史稀疏或不可用的现实场景中的个性化文本生成任务。实验结果表明，PGraphRAG在多样化任务上显著优于最先进的个性化方法，展示了基于图的检索在个性化方面的独特优势。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
