Real ChatGPT answer · index seedVtBHxpcPCcIcompleteB2B software · AI and automation toolsinformational
How do we fine-tune embedding models to improve semantic search relevance for industry-specific medical terminology?
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Fan-out diagram
The searches the AI actually ran for this prompt.
SEED QUERY
How do we fine-tune embedding models to improve semantic search relevance for industry-specific medical terminology?
1 real search · 1 real-search pass
medical domain embedding model fine tuning semantic retrieval SapBERT contrastive learning biomedical information retrieval evaluationGoogle ↗
OBSERVED
Badge and edge weight = share of passes that produced the query. Hover a query for its intent and routing format.
Query classes
Google's semantic query class for every sub-query: what kind of answer each search is after, simulated vs real.
Classifying 1 queries…
Searches and sources
ChatGPT1 real searches · gpt-6
medical domain embedding model fine tuning semantic retrieval SapBERT contrastive learning biomedical information retrieval evaluation Google ↗
- MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval · pmc.ncbi.nlm.nih.gov
- MedCPT: Contrastive Pre-trained Transformers with large-scale PubMed search logs for zero-shot biomedical information retrieval · pubmed.ncbi.nlm.nih.gov
- Journal of Medical Internet Research - Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study · jmir.org
- Journal of Medical Internet Research - Improving Retrieval Augmented Generation for Health Care by Fine-Tuning Clinical Embedding Models: Development and Evaluation Study · jmir.org