  Unlike professional caregivers, family caregivers often assume this role
without formal preparation or training. Because of this, there is an urgent
need to enhance the capacity of family caregivers to provide quality care.
Large language models can potentially be used as a foundation technology for
supporting caregivers as educational tools or as adjunct to care. This study
aimed to develop a reliable Caregiving Language Model (CaLM) by using FMs and a
caregiving knowledge base, develop an accessible CaLM using a small FM that
requires fewer computing resources, and evaluate the performance of the model
compared to a large FM. We developed CaLM using the Retrieval Augmented
Generation (RAG) framework combined with FM fine-tuning for improving the
quality of FM answers by grounding the model on a caregiving knowledge base. We
used two small FMs as candidates for the FM of CaLM (LLaMA-2 and Falcon with 7B
parameters) and larger FM GPT-3.5 as a benchmark. We developed the caregiving
knowledge base by gathering various types of documents from the Internet. In
this study, we focused on caregivers of individuals with Alzheimer's Disease
Related Dementias. We evaluated the models' performance using the benchmark
metrics commonly used in evaluating language models and their reliability to
provide accurate references with the answers. The RAG framework improved the
performance of all FMs used in this study across all measures. As expected, the
large FM performed better than small FMs across all metrics. The most
interesting result is that small fine-tuned FMs with RAG performed
significantly better than GPT 3.5 across all metrics. The fine-tuned LLaMA-2
small FM performed better than GPT 3.5 (even with RAG) in returning references
with the answers. The study shows that reliable and accessible CaLM can be
developed by using small FMs with a knowledge base specific to the caregiving
domain.
