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Recent research papers have introduced new techniques to enhance the capabilities of large language models (LLMs) such as Multi-Head RAG (MRAG), Mixture-of-Agents (MoA), RE-RAG, DomainRAG, Tree-RAG, and DR-RAG. These methods aim to improve retrieval accuracy, relevance, performance, and interpretability in language tasks by leveraging different approaches like multi-aspect document retrieval, dynamic document relevance, and context relevance estimation. MoA leads in AlpacaEval 2.0 with a score of 65.1% surpassing GPT-4 Omni at 57.5%.
DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering Improves document retrieval recall and answer accuracy for knowledge-intensive tasks by mining static and dynamic document relevance. 📝https://t.co/RJd3Np1rQg https://t.co/NnLF8qNgvP
Tree-RAG (T-RAG), an enhanced RAG technique. ✨ Paper - "T-RAG: Lessons from the LLM Trenches" 📌 Combines the use of RAG with a finetuned open-source LLM. T-RAG, uses a tree structure to represent entity hierarchies within the organization. This is used to generate a textual… https://t.co/S1swvloeke
A new paper suggests a novel framework that outperforms existing RAG methods by up to 20% while being faster and cheaper. Language models have made incredible strides, but they still struggle with integrating new information without forgetting the old. Enter HippoRAG - a new… https://t.co/QDnjdZMTOv
