Prompting Large Language Models to Detect Dementia Family Caregivers
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 12 references that are in the map. First posted on arXiv 4 Aug 2025.
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References · 7
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
- 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
- 2020BERTweet: A pre-trained language model for English Tweetscited by 346
- 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 184
- 2024Cascade Prompt Learning for Vision-Language Model Adaptationcited by 19
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