Related Experiment Video
Updated: Jun 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Using large language models to assess public perceptions around glucagon-like peptide-1 receptor agonists on social
Sulaiman Somani1, Sneha S Jain2, Ashish Sarraju3
1Department of Medicine, Stanford University, Stanford, CA, USA.
Background:
The prevalence of obesity has been increasing worldwide, with substantial implications for public health. Obesity is independently associated with cardiovascular morbidity and mortality and is estimated to cost the health system over $200 billion dollars annually. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as a practice-changing therapy for weight loss and cardiovascular risk reduction independent of diabetes.
Methods:
We used large language models to augment our previously reported artificial intelligence-enabled topic modeling pipeline to analyze over 390,000 unique GLP-1 RA-related Reddit discussions.
Results:
We find high interest around GLP-1 RAs, with a total of 168 topics and 33 groups focused on the GLP-1 RA experience with weight loss, comparison of side effects between differing GLP-1 RAs and alternate therapies, issues with GLP-1 RA access and supply, and the positive psychological benefits of GLP-1 RAs and associated weight loss. Notably, public sentiment in these discussions was mostly neutral-to-positive.
Conclusions:
These findings have important implications for monitoring new side effects not captured in randomized control trials and understanding the public health challenge of drug shortages.
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