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Researchers are exploring methods to enhance Large Language Models (LLMs) by combining language understanding abilities with reasoning abilities in a Retrieval-Augmented Generation (RAG) style. Techniques such as fine-tuning and RAG are being compared for optimizing LLM performance. GNN-RAG, a novel AI method, aims to outperform GPT-4 by combining LLMs with Graph Neural Networks (GNNs) for reasoning. The synergy between technologies like RAG and AI is enabling the creation of efficient data retrieval agents.
🤖🇺🇸 Is RAG the Future of AI? Discover how Retrieval-Augmented Generation (RAG) could transform generative AI by eliminating "hallucinations" and enhancing reliability and productivity. A game-changer for businesses relying on AI! https://t.co/IXaisCIRjG
Businesses can unlock domain-specific, real-time data for generative AI through #RAG. Download the e-book to learn how to set up workflows for building #AI assistants and chatbots. https://t.co/idTgqC4rxF https://t.co/uEYOBrVQzd
👨🏻💻 Demo alert! 👩🏽💻 Discover how to build an AI application that uses RAG (Retrieval Augmented Generation) without the need for coding — and get it running in minutes. Watch the @TechWithTimm video ⬇️ https://t.co/gfY2McxI5u #Langflow #RAGApplications #DeveloperCommunity
great new article on optimizing LLMs for accuracy, answering questions like when to just prompt engineer vs when to fine-tune or use RAG (or both) https://t.co/O3KBzeF30P
Optimizing LLMs for accuracy can be challenging. @colintjarvis has distilled a year’s worth of insights into a new practical guide. Discover how to start optimizing, choose the right methods, and achieve production-ready accuracy: https://t.co/G4pmJE16jd https://t.co/dgOFPZ5UdM
🤖 From this week's issue: Fine-tuning is the process of taking a pre-trained LLM and adapting it to a specific task or domain. https://t.co/XYxVQsWcNs
i have generally been down on RAG as ive found most of the implementations terribly naive. was forced to put together a few pipelines for demos: smart semantic chunking + scann + reranking + BM25 is actually pretty fucking solid even for moderately complex data.
Strategies for optimizing LLM accuracy through prompt engineering, RAG, and fine-tuning https://t.co/tBEKzdBrIx
Make room for RAG: How Gen AI's balance of power is shifting AI's RAG is not a panacea for hallucinations, but it's here to stay, which may mean ultimately adapting the training or large language models to better accommodate RAG. https://t.co/ovZbdllJMF @OpenAI @elastic…
💡 This webinar will explore how the synergy between these technologies enables the creation and management of RAG agents, which are essential for efficient data retrieval and decision-making in real-time. Register: https://t.co/bivrPIHe79 #AI #data #RAG #SingleStore
From Greenblatt et al. at Redwood: Subtle, interesting results on how and when we can rely on fine-tuning-based evaluations to elicit capabilities from LLMs. https://t.co/NU0XRMrNLM
Graph NNs+RAG for Reasoning. This paper introduces a novel method for combining language understanding abilities of LLMs with the reasoning abilities of GNNs in RAG style. The researches claim that GNN-RAG achieves SOTA outperforming or matching GPT-4. GNN-RAG excels on…
Optimizing LLM Performance: RAG vs Fine-tuning 🤔 Large Language Models (LLMs) can be optimized using two prominent techniques: Retrieval-Augmented Generation (RAG) and fine-tuning. Each method has unique strengths and weaknesses, making it crucial to understand when to use them…
GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style https://t.co/BIAyMihHgd #GNNRAG #AI #KGQA #ArtificialIntelligence #TechInnovation #ai #news #llm #ml #researc… https://t.co/gv4Ed00qiZ
GNN-RAG: A Novel AI Method for Combining Language Understanding Abilities of LLMs with the Reasoning Abilities of GNNs in a Retrieval-Augmented Generation (RAG) Style Researchers from the University of Minnesota introduced GNN-RAG, an efficient approach for enhancing RAG in… https://t.co/qF4hA1PyCp
Retrieval-augmented generation (RAG) helps address some issues, but retrieval accuracy is a major bottleneck for end-to-end performance. https://t.co/dlOIPCuaMd #Database #AIEngineering #LargeLanguageModels @MyScaleDB
Can LLMs learn from a single example? #Learn #LLMs #Single Summary: recently while fine-tuning... https://t.co/LkeBPD5GUj