用大模型提示词识别社交平台上照护者发的痴呆相关推文。
Prompting Large Language Models to Detect Dementia Family Caregivers
- 用微调后的大模型配合简单零样本提示，实现高效分类。
- 在验证集和测试集上均达到0.95的宏F1分数。
- 适合做线上照护者支持系统的情感与需求分析。
社交媒体如推特为痴呆患者家属分享经历、寻求支持提供了机会。这些在线信息也为开发基于网络的干预措施创造了条件。然而，首要任务是识别出家属发布的涉及痴呆的推文。本文展示了针对SMM4H 2025共享任务3的系统，该任务旨在检测提及家庭成员患痴呆的推文。任务被定义为二分类问题，区分提及家庭成员患痴呆的推文与不相关的推文。我们的解决方案探索了多种提示方法的大语言模型（LLMs）。结果表明，在微调模型上使用简单的零样本提示取得了最佳效果。最终系统在验证集和测试集上均达到0.95的宏F1分数。完整代码已公开于GitHub。
原文摘要 · Abstract (English)
Social media, such as Twitter, provides opportunities for caregivers of dementia patients to share their experiences and seek support for a variety of reasons. Availability of this information online also paves the way for the development of internet-based interventions in their support. However, for this purpose, tweets written by caregivers of dementia patients must first be identified. This paper demonstrates our system for the SMM4H 2025 shared task 3, which focuses on detecting tweets posted by individuals who have a family member with dementia. The task is outlined as a binary classification problem, differentiating between tweets that mention dementia in the context of a family member and those that do not. Our solution to this problem explores large language models (LLMs) with various prompting methods. Our results show that a simple zero-shot prompt on a fine-tuned model yielded the best results. Our final system achieved a macro F1-score of 0.95 on the validation set and the test set. Our full code is available on GitHub.
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