Nvidia’s NeMo Retriever models and RAG pipeline make quick work of ingesting PDFs and generating reports based on them. Chalk one up for the plan-reflect-refine architecture.
A method that uses instruction tuning to adapt LLMs for knowledge-intensive tasks. RankRAG simultaneously trains the models for context ranking and answer generation, enhancing their retrieval-augmented generation (RAG) capabilities.
NVIDIA and Georgia Tech researchers introduce RankRAG, a novel framework instruction-tuning a single LLM for top-k context ranking and answer generation. Aiming to improve RAG systems, it enhances context relevance assessment and answer generation.
Windows only
