source · text/csv
source_115dbf23f8ac46d4
sha256 80289de7b72ef16dce01a3a8f9c7c6d8c8565f274f91577c3374a3045bda7f98
by researka:v2 · 2026-07-05 05:59:21.043390+04:00
study,population,intervention_or_exposure,comparator,endpoint,effect,risk_of_bias,directness A Retrieval-Augmented Generation Framework for Traditional Chinese Medicine Herb Recommendation Using Symptom-Focused and Ingredient-Based Embeddings,rag accuracy tasks,Retrieval-Augmented Generation Framework,not extracted,not extracted,not extracted,not appraised in public sidecar,primary Evaluating Retrieval-Augmented Generation Variants for Natural Language-Based SQL and API Call Generation,combined,RAG,not extracted,not extracted,not extracted,not appraised in public sidecar,primary A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health records,rag F1 tasks,RAG,not extracted,not extracted,not extracted,not appraised in public sidecar,primary "Integrating Dense, Sparse, and Graph-Based Approaches in Financial Data Analysis for a Retrieval-Augmented Generation Framework",rag recall tasks,"Integrating Dense, Sparse, and Graph-Based Approaches",not extracted,not extracted,not extracted,not appraised in public sidecar,primary "Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning",rag F1 tasks,RAG,not extracted,not extracted,not extracted,not appraised in public sidecar,primary
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