Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
Researchers explored the effectiveness of generalistic versus specific embeddings for semantic search in clinical coding across non-English languages. They found that fine-tuning a Spanish biomedical encoder with LLM-generated synthetic data significantly improved performance in languages like Spanish, Catalan, French, and Portuguese. This approach, involving a bi-encoder and a cross-encoder reranker, even surpassed existing English-based models on certain metrics without English biomedical pretraining. AI
IMPACT Demonstrates a method for improving non-English language model performance in specialized domains using synthetic data.