arXiv cs.AI· Sophia Mao, Keshav Gandhi, Alexander Gutfraind·· 3 天前AI 评分22
大语言模型生成的行为模式能否用于流行病学建模：多基准评估框架
Large language model-generated patterns for epidemiological modeling: a multi-benchmark evaluation framework
AI 导读
研究以注射吸毒人群的丙型肝炎相关行为为例，评估 LLM 生成的行为输出能否表征个体行为异质性。在三个 LLM 中，28 项文献关联的无条件方向恢复率为 46-68%，而直接让 LLM 分类同一批关联时一致率达 82-93%；与 ABM 和调查相关模式的平均绝对差异分别为 0.11-0.13 和 0.15-0.18。
正文 · 原文
Abstract:Representing individual-level behavioral heterogeneity is challenging in epidemiological modeling, particularly when empirical behavioral data are sparse. We assess the potential of large language model (LLM)-generated behavioral outputs in addressing this challenge, using hepatitis C virus-related behaviors among people who inject drugs as a case study. Correspondence with epidemiological patterns was evaluated using external benchmarks: literature-informed association directions, correlations from an agent-based model (ABM), and survey associations. Across three LLMs, unconditional directional recovery across 28 retained literature-informed associations was 46-68%, compared with 82-93% agreement when LLMs were directly asked to classify the same associations. Mean absolute differences from ABM and survey correlation patterns were 0.11-0.13 and 0.15-0.18, respectively. Together, these findings show partial alignment between LLM-generated behavioral relationships and epidemiological evidence, despite higher knowledge-probe agreement. More systematic evaluation is needed before incorporating LLM-generated behavioral outputs into epidemiological models that simulate transmission dynamics.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.15994 [cs.AI] |
| (or arXiv:2610.15994v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.15994 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sophia Mao [view email]
[v1]
Sun, 11 Oct 2026 23:48:02 UTC (263 KB)
来源：arXiv cs.AI · arxiv.org