Retrieval Augmented Generation (RAG) is a new approach that combines text generation with information retrieval to enhance language model output. It involves a retriever suggesting relevant steps and table names based on natural language input. The concept aims to reduce hallucination in structured outputs.
Retrieval Augmented Generation: Where Information Retrieval Meets Text Generation This article introduces retrieval augmented generation, which combines text generation with informaton retrieval in order to improve language model output. https://t.co/uGuZkvwT1Z https://t.co/gBdgQDBijd
Evaluating Retrieval Quality in Retrieval-Augmented Generation 📝https://t.co/wrTSxeqYBB 👨🏽💻https://t.co/CrEIfcvLHy https://t.co/YDYzr09GQk
Advanced Retrieval-Augmented Generation: From Theory to LlamaIndex Implementation https://t.co/yjJaJbUS5r #AI #MachineLearning #DeepLearning #LLMs #DataScience https://t.co/zxR2GJVm8c
Paper - "Reducing hallucination in structured outputs via Retrieval-Augmented Generation" The team implemented a Retrieval-Augmented Generation (RAG) approach, where a retriever suggests relevant steps and table names based on the user's natural language input. These suggestions… https://t.co/AA5c0txUPV
What is Retrieval Augmented Generation? How it Works & Use Cases https://t.co/8Vv4yHDSyd