Recent discussions in the AI community focus on Retrieval Augmented Generation (RAG), a method that enhances AI's knowledge by integrating data into its system. This approach leverages Large Language Models (LLMs) to improve retrieval capability, reduce training costs, and enhance performance on low-frequency entities in question-answering tasks. RAG is seen as a reliable and adaptable solution that outperforms traditional parametric models, with ongoing efforts to advance its implementation across various scenarios like code, image, and audio.
Each technique is a different strategy to help the AI understand a question and provide a more accurate and relevant answer by using provided information Read more about RAG in this blog: https://t.co/RBXJRxtW2k #RAG #retriever #retrieval #generation https://t.co/P14llz9rOV
RAG for AI-Generated Content Cool survey paper providing an overview of RAG used in different generation scenarios like code, image, audio,... I like the taxonomy of RAG enhancements and it seems to mention a lot of key RAG papers. https://t.co/73wi0nKEx8
In this blog, you will learn how to implement Retrieval Augmented Generation (RAG) using Weaviate, LangChain4j and LocalAI. This allows you to ask questions about your documents using natural language. @weaviate_io @langchain4j @LocalAI_API #java https://t.co/Ts7pufUSt5
Reliable, Adaptable, and Attributable Language Models with Retrieval Argues that retrieval-augmented language models can be more reliable and adaptable than traditional parametric models, and proposes a roadmap for their advancement. 📝https://t.co/if9ukPUcS9 https://t.co/1kyf30kQnp
Self-Retrieval: Building an Information Retrieval System with One Large Language Model Proposes an end-to-end, LLM-based IR system that leverages the LLM's capabilities for indexing, retrieval, and self-assessment. 📝https://t.co/P3syPrFQrV 👨🏽💻https://t.co/0FVMrA25cf https://t.co/F9Ux1SRqW3
Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge Compares fine-tuning and RAG to improve the performance of LLMs on low-frequency entities in question-answering tasks. 📝https://t.co/aopEuRjUwi 👨🏽💻https://t.co/9M4pzH5a3g https://t.co/pnCsniiWgS
LLM-Oriented Retrieval Tuner Proposes a method to improve the retrieval capability of LLMs by leveraging their existing representations without fine-tuning. Achieves strong performance and reduces training cost compared to fine-tuning the entire LLM. 📝https://t.co/4KnyJ84Hth https://t.co/cSGeY1Juc5
Are your #AI answers stuck in the past? #RAG presents a solution by integrating your data into the AI's knowledge. 💡 This pattern uses embedding models and Large Language Models (LLMs). Wanna get a little nerdy? 🤓 Follow along to see how it works. ⬇️ 🧵 1/5