用用户知识图谱提升大模型个性化生成效果
Personalized Graph-Based Retrieval for Large Language Models
- 构建用户中心的知识图谱，通过图结构增强检索与生成
- 在冷启动场景下仍显著优于现有方法，提升生成质量
- 专为稀疏历史数据设计，适合真实应用中的个性化需求
随着大语言模型的发展，其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变革性潜力。然而，现有个性化方法通常仅依赖用户历史来扩充提示，限制了在冷启动场景（数据稀疏）下生成定制化输出的效果。为此，我们提出个性化图基检索增强生成框架（PGraphRAG），利用以用户为中心的知识图谱丰富个性化信息。通过将结构化的用户知识直接融入检索过程，并用用户相关上下文增强提示，PGraphRAG提升了上下文理解能力与输出质量。同时，我们构建了面向文本生成的个性化图基基准测试集，用于评估真实场景中用户历史稀疏或缺失时的个性化生成性能。实验表明，PGraphRAG在多种任务上显著优于现有先进方法，验证了图基检索在个性化生成中的独特优势。
原文摘要 · Abstract (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.
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