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The content discusses the various stages of augmentation in knowledge-intensive processes, focusing on methodologies for pretraining, fine-tuning, and inference. It highlights methods such as REALM, RETRO, ATLAS, COG, and RETRO++ for pretraining, emphasizing the importance of fine-tuning for model adaptation to specific tasks. Advanced inferencing techniques like DSP, PKG, CREA-ICL, and RE-CITE are also explored, along with the impact of augmentation source selection on RAG model effectiveness.
Different types of data, including unstructured and structured data, are discussed, with examples like FLARE and RET-LLMs for active retrieval and knowledge graphs for high-quality context. Researchers are also exploring generated content of LLMs in RAG models to address limitations of external auxiliary information. The content also touches on the iterative and recursive retrieval methods, as well as adaptive retrieval techniques like FLARE and SelfRAG.
The article concludes by mentioning upcoming in-depth details, comparisons, future prospects, and demos related to the discussed topics. References to further reading sources like arXiv, NVIDIA Blogs, Huggingface Blogs, and Microsoft Blogs are provided for additional information.
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Source link: https://vishwanathkamath.medium.com/enhancing-retrieval-augmented-generation-rag-methods-and-innovations-augmentation-in-rag-a7106a3f578e?source=rss——ai-5
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