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<p>Social media is a critical platform for understanding and fostering public engagement with health interventions. However, the lack of real-time social media infoveillance on public health issues may lead to delayed responses and suboptimal policy adjustments. To address this gap, we developed PH-LLM—a novel suite of large language models (LLMs) designed for real-time public health monitoring. We curated a multilingual training corpus and trained PH-LLM using QLoRA and LoRA plus, leveraging Qwen 2.5. We constructed a benchmark comprising 19 English and 20 multilingual held-out tasks and evaluated PH-LLM’s zero-shot performance. PH-LLM consistently outperformed baseline LLMs of similar and larger sizes. PH-LLM-14B and PH-LLM-32B surpassed Qwen2.5-72B-Instruct, Llama-3.1-70B-Instruct, Mistral-Large-Instruct-2407, and GPT-4o in both English tasks (&gt;=56.0% vs. &lt;= 52.3%) and multilingual tasks (&gt;=59.6% vs. &lt;= 59.1%). PH-LLM represents a significant advancement in real-time public health infoveillance, offering state-of-the-art multilingual capabilities and cost-effective solutions for monitoring public sentiment on health issues.</p>

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A suite of large language models for public health infoveillance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月23日

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  • Xinyu Zhou,
  • Jiaqi Zhou,
  • Chiyu Wang,
  • Qianqian Xie,
  • Kaize Ding,
  • Chengsheng Mao,
  • Yuntian Liu,
  • Zhiyuan Cao,
  • Huangrui Chu,
  • Xi Chen,
  • Hua Xu,
  • Heidi J. Larson,
  • Yuan Luo

摘要

Social media is a critical platform for understanding and fostering public engagement with health interventions. However, the lack of real-time social media infoveillance on public health issues may lead to delayed responses and suboptimal policy adjustments. To address this gap, we developed PH-LLM—a novel suite of large language models (LLMs) designed for real-time public health monitoring. We curated a multilingual training corpus and trained PH-LLM using QLoRA and LoRA plus, leveraging Qwen 2.5. We constructed a benchmark comprising 19 English and 20 multilingual held-out tasks and evaluated PH-LLM’s zero-shot performance. PH-LLM consistently outperformed baseline LLMs of similar and larger sizes. PH-LLM-14B and PH-LLM-32B surpassed Qwen2.5-72B-Instruct, Llama-3.1-70B-Instruct, Mistral-Large-Instruct-2407, and GPT-4o in both English tasks (>=56.0% vs. <= 52.3%) and multilingual tasks (>=59.6% vs. <= 59.1%). PH-LLM represents a significant advancement in real-time public health infoveillance, offering state-of-the-art multilingual capabilities and cost-effective solutions for monitoring public sentiment on health issues.