Author：Deephub Imba This article is approximately 2800 words, recommended reading time 5minutes This article introduces how the diffusion model works. The rise of diffusion models can be seen as a major factor in the recent breakthroughs in the field of AI-generated art. The development of the stable diffusion model allows us to easily create wonderful … Read more
Large Language Models (LLMs) have made significant progress in the field of generative artificial intelligence, but they face the “hallucination” problem, which is the tendency to generate inaccurate or irrelevant information. This issue is particularly severe in high-risk applications such as medical assessments and insurance claims processing. To address this challenge, researchers from the University … Read more
What is RAG Technology RAG, or Retrieval-Augmented Generation technology, is an AI architecture that combines retrieval and generation. It enhances the output quality of language models by retrieving external knowledge. This is akin to equipping large language models with an intelligent knowledge base, enabling them to retrieve and reference relevant information in real-time while generating … Read more
Roaming RAG, as an innovative RAG technology, can be elaborated on in detail regarding its characteristics and advantages from the following aspects: Working Principle and Process The core of Roaming RAG lies in utilizing the hierarchical structure of documents to enhance the information retrieval capabilities of large language models (LLMs). The specific process includes: Document … Read more
The RAG technology introduces external knowledge retrieval in the AI generation process, evolving from basic document queries to intelligent architectures with multi-modal and Multi-Agent collaborations, allowing AI to provide more accurate and comprehensive answers. Core Components: Embedding Model: Converts text into vector representations. Generation Model: Responsible for the final content generation. Re-ranking Model: Optimizes the … Read more
Source: Deep Learning and Large Models (LLM) This article is approximately 3500 words long and is recommended for a 9-minute read. This article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework adopts a … Read more
Machine Heart Report Editor: Rome Retrieval-Augmented Generation (RAG) and Fine-tuning are two common methods to enhance the performance of large language models. So, which method is better? Which is more efficient when building applications in specific domains? This paper from Microsoft serves as a reference for your choice. When constructing large language model applications, there … Read more
Follow us to stay updated! Recently, the LLM community has been immersed in the shock brought by DeepSeek-V3. This model is not only open-source but also performs well. However, such a large-scale LLM is beyond our reach (the GPU memory can’t handle it). If we can’t afford that, let’s take a look at Microsoft’s open-source … Read more
Since OpenAI officially released ChatGPT in November 2022, the AI technology ecosystem has experienced rapid advancement. The public has transitioned from a state of confusion to a thrilling and exciting experience, and now to feelings of unease due to the cost-cutting and efficiency improvements brought by their respective companies. The world is changing so quickly, … Read more
Recently, the world-renowned management consulting firm Boston Consulting Group (BCG) released a highly insightful report predicting that AI Agents will spark a revolution across various industries, prompting profound reflections on future work models, business models, and even the shape of human society. As Yuval Noah Harari, author of “Sapiens: A Brief History of Humankind,” stated: … Read more