A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
Augmenting black-box LLMs with medical textbooks for biomedical question answering
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Domain fine-tuning of a 4B LLM yields a statistically significant 6.8 pp accuracy gain on MedQA-USMLE over a general baseline, while RAG over medical explanations produces no significant improvement.
citing papers explorer
-
From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
A survey that defines Compound AI Systems, proposes a multi-dimensional taxonomy based on component roles and orchestration strategies, reviews four foundational paradigms, and identifies key challenges for future research.
-
Domain Fine-Tuning vs. Retrieval-Augmented Generation for Medical Multiple-Choice Question Answering: A Controlled Comparison at the 4B-Parameter Scale
Domain fine-tuning of a 4B LLM yields a statistically significant 6.8 pp accuracy gain on MedQA-USMLE over a general baseline, while RAG over medical explanations produces no significant improvement.