LLM (Large Language Model) is a powerful new platform, but they are not always trained on data that is relevant to our tasks or the most recent data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private data or the latest data). It allows LLMs to … 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
LLM (Large Language Model) is a powerful new platform, but they are not always trained on data relevant to our tasks or the latest data. RAG (Retrieval Augmented Generation) is a general method that connects LLMs with external data sources (such as private or up-to-date data). It allows LLMs to use external data to generate … 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
A Survey of Query Optimization in Large Language Models Paper Link:https://arxiv.org/pdf/2412.17558 Published by: Tencent Large Language Models (LLMs) are becoming increasingly popular, but they also face challenges such as “hallucination” when dealing with domain-specific tasks or those requiring specialized knowledge. Retrieval-Augmented Generation (RAG) technology has emerged as a key method for enhancing model performance, with … 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
Editor’s Overview This reading list published by Latent Space selects 50 papers in the field of AI engineering, covering ten core modules, providing valuable resources for AI engineers and other professionals to enhance their skills. The cutting-edge large language model (LLM) section covers the latest developments of major series models; the benchmarking and evaluation module … Read more
Recently, while reading some materials about RAG systems, I discovered an interesting phenomenon: the relevance issue of RAG is far more complex than we imagine. Whether from the perspective of data retrieval or the understanding of relevance by large models, the performance of RAG is filled with challenges and opportunities. Today, I would like to … Read more
In recent years, artificial intelligence has made significant leaps, primarily due to large language models (LLMs). LLMs are very good at understanding and generating human-like text, leading to the creation of various new tools, such as advanced chatbots and AI writers. Although LLMs excel at generating fluent, human-like text, they sometimes struggle with factual accuracy. … Read more
Abstract With the growing popularity of Retrieval-Augmented Generation (RAG) systems in various real-world services, concerns about their security are increasing. RAG systems enhance the generative capabilities of Large Language Models (LLMs) through retrieval mechanisms operating on private knowledge bases. However, unintended exposure of this mechanism can lead to severe consequences, including the leakage of private … Read more