Improving accuracy of GPT-3/4 results on biomedical data using a retrieval-augmented language model

Understand

Large language models (LLMs) have made significant advancements in natural language processing (NLP).

• Broad corpora capture diverse patterns but can introduce irrelevance, while focused corpora enhance reliability by reducing misleading information.
• Training LLMs on focused corpora poses computational challenges.
• An alternative approach is to use a retrieval-augmentation (RetA) method tested in a specific domain.