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
Click 👇🏻 to follow, article from “ With the expanding application of large language models (LLMs), Retrieval-Augmented Generation (RAG) has become a mature technology. The popularity of products like txt2sql and ChatBI highlights the increasing importance of query construction techniques. This article analyzes the process of query construction and illustrates, through examples, how to transform … Read more
Are you still troubled by the mixed quality of AI in China and its poor performance? Then let’s take a look at Developer Cat AI (3in1)! This is an integrated AI assistant that combines GPT-4, Claude3, and Gemini. It covers all models of the three AI tools. Including GPT-4o and Gemini flash Now you can … 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
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
Recently, it feels like the New Year has come early. Just last night, DeepSeek and Kimi both released their version 1.0, and Kimi was the first to publish its technical report, which is quite interesting… When it comes to Kimi, everyone has the impression that it has a technological first-mover advantage, being the first to … Read more
Hello everyone, I am Liu Cong from NLP. Just tonight, Kimi released the latest model K1.5, first, let’s take a look at the leaderboard results, it’s simply explosive. In long reasoning, K1.5 far surpasses OpenAI’s O1 model in mathematical ability, whether in pure text or visual multimodal; it is on par with Codeforces, slightly lagging … 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