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
Seeing Through Multiple Lenses: Multi-Head RAG Leverages Transformer Power for Improved Multi-Aspect Document Retrieval https://t.co/rCnVS5Clor #MRAG #LanguageModels #DocumentRetrieval #AI #Transformers #ai #news #llm #ml #research #ainews #innovation #artificialintelligence … https://t.co/4ikZ6jJMZx
How did a Mixture-of-Agents method Achieves SotA performance and surpassing GPT4o on Alpaca & MT-Bench? Let's have a look at Mixture-of-Agents Enhances Large Language Model Capabilities 👇👇 https://t.co/kS63WP6WeT
How should you tackle the challenges of retrieving and evaluating relevant context for RAG? @helloiamleonie turns to Ragas, TruLens, and DeepEval to assess their performance in this increasingly crucial task. https://t.co/OUCy9rmdjK
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation Evaluates RAG models on six crucial abilities in a domain-specific scenario through a comprehensive dataset called DomainRAG. 📝https://t.co/mWGxmQfdFm 👨🏽💻https://t.co/9TY3XyPl3x https://t.co/6dq58JWW5T
RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation Proposes a framework that extends the RAG approach by incorporating an explicit context relevance estimator. 📝https://t.co/azkXm6SMAb https://t.co/bUP75PmQbQ
Very interesting Paper - "Mixture-of-Agents (MoA) Enhances Large Language Model Capabilities": - MoA using only open-source LLMs is the leader of AlpacaEval 2.0 by a substantial gap, achieving a score of 65.1% compared to 57.5% by GPT-4 Omni. 🔥 📌 The paper introduces the… https://t.co/P09kddjZMt
[CL] Multi-Head RAG: Solving Multi-Aspect Problems with LLMs M Besta, A Kubicek, R Niggli, R Gerstenberger... [ETH Zurich] (2024) https://t.co/ZpG0WEtoWi - Multi-Head RAG (MRAG) is a new scheme that leverages activations from the multi-head attention layer instead of the… https://t.co/9Fyfeu7tWT
[CL] Mixture-of-Agents Enhances Large Language Model Capabilities J Wang, J Wang, B Athiwaratkun, C Zhang, J Zou [Duke University & Together AI] (2024) https://t.co/G0MwggzhDt - Recent advances in large language models (LLMs) show great capabilities in language tasks. However,… https://t.co/yKbAHBWJKL
Multi-Head RAG (MRAG), aims to improve retrieval accuracy for complex queries requiring that require fetching multiple documents with substantially different contents. Real-world use cases to demonstrate MRAG improves up to 20% in relevance over standard RAG baselines. Paper -… https://t.co/mR4M5Tgga8