用图结构动态筛选可穿戴数据，让大模型更精准地分析个人健康信息。
Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data
- 将可穿戴数据构建成个性化知识图谱，按问题自动提取相关片段。
- 在真实数据上测试，比基线方法和标准RAG高出约70%的胜率。
- 适合做个性化健康分析的大模型研究者与开发者参考。
大语言模型（LLMs）正被用于分析长期、多模态且高度个性化的可穿戴传感数据。核心挑战在于上下文选择：提供不足会限制推理能力，而包含全部数据则导致效率低下且生成质量下降。本文提出可穿戴即图（Wearable As Graph, WAG），一种基于图的上下文检索框架，支持大模型对可穿戴数据进行查询自适应推理。WAG将可穿戴指标与用户特定信号组织成个性化知识图谱，并检索与查询相关的子图以支持下游生成。检索过程融合全局关系（通过分层贝叶斯建模捕捉群体与个体层面的模式）和局部关系（反映短期信号偏差），并引入查询开放度信号控制检索范围。我们在超过10,000个来自真实可穿戴数据集的数据驱动问题上评估了WAG。在基于大模型和人工的双重评估中，WAG相比基线方法和标准RAG方法取得了约70%的胜率，验证了结构化、查询自适应上下文检索在大模型驱动可穿戴数据分析中的有效性。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly applied to analyzing wearable sensing data, which are long-term, multimodal, and highly personalized. A key challenge is context selection: providing insufficient context limits reasoning, while including all available data leads to inefficiency and degraded generation quality. We propose Wearable As Graph (WAG), a graph-based context retrieval framework that enables query-adaptive reasoning over wearable data with LLMs. WAG organizes wearable metrics and user-specific signals into a personalized knowledge graph, and retrieves a query-conditioned subgraph to support downstream generation. The retrieval process integrates global relationships, capturing prior knowledge and population- and individual-level patterns via hierarchical Bayesian modeling, with local relationships that reflect short-term signal deviations. A query openness signal further controls retrieval breadth. We evaluate WAG on over 10,000 data-grounded queries from real-world wearable datasets. Across LLM-based and human evaluations, WAG achieves an approximately 70% win rate over baseline and standard RAG methods, demonstrating the effectiveness of structured, query-adaptive context retrieval for LLM-driven analysis of wearable data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。