LifeAgentBench is a comprehensive, large-scale Question Answering (QA) benchmark specifically developed to assess the capabilities of Large Language Models (LLMs) in the complex domain of personalized digital health support. It addresses a critical gap by providing a systematic framework for evaluating LLMs on tasks requiring long-horizon, cross-dimensional reasoning over heterogeneous lifestyle signals, which is essential for effective health assistance. The benchmark includes 22,573 questions, ranging from basic information retrieval to intricate reasoning scenarios. It also offers an extensible construction pipeline and a standardized evaluation protocol, ensuring reliable and scalable assessment. By identifying key bottlenecks in LLM performance, particularly in long-horizon aggregation and cross-dimensional reasoning, LifeAgentBench enables researchers and ML engineers to develop more robust and effective LLM-based health assistants, such as the proposed LifeAgent baseline.
LifeAgentBench is a new, large-scale test designed to evaluate how well AI models, specifically large language models, can provide personalized health advice over time using various health data. It helps researchers understand the limitations of current AI in offering smart, long-term health support and guides the development of better health assistants.
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