A scalable framework for benchmark embedding models in semantic health-care tasks

Text embeddings are promising for semantic tasks, such as retrieval augmented generation (RAG). However, their application in health care is underexplored due to a lack of benchmarking methods. We introduce a scalable benchmarking method to test embeddings for health-care semantic tasks. We evaluated 39 embedding models across 7 medical semantic similarity tasks using diverse datasets. These datasets comprised real-world patient data (from the Mount Sinai Health System and MIMIC IV), biomedical texts from PubMed, and synthetic data generated with Llama-3-70b. We first assessed semantic textual similarity (STS) by correlating the model-generated similarity scores with noise levels using Spearman rank correlation. We then reframed the same tasks as retrieval problems, evaluated by mean reciprocal rank and recall at k. In total, evaluating 2000 text pairs per 7 tasks for STS and retrieval yielded 3.28 million model assessments. Larger models (>7b parameters), such as those based on Mistral-7b and Gemma-2-9b, consistently performed well, especially in long-context tasks. The NV-Embed-v1 model (7b parameters), although top in short tasks, underperformed in long tasks. For short tasks, smaller models such as b1ade-embed (335M parameters) performed on-par to the larger models. For long retrieval tasks, the larger models significantly outperformed the smaller ones. The proposed benchmarking framework demonstrates scalability and flexibility, offering a structured approach to guide the selection of embedding models for a wide range of health-care tasks. By matching the appropriate model with the task, the framework enables more effective deployment of embedding models, enhancing critical applications such as semantic search and retrieval-augmented generation (RAG).