A suite of large language models for public health infoveillance.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41731011.
- Also identified by DOI 10.1038/s41746-026-02435-6 and PMC identifier 13039744.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
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.