Source: DeepHub IMBA This article is approximately 7000 words long and is recommended to be read in over 10 minutes. This article delves into the LLM Graph Transformer framework of LangChain and its dual-mode implementation mechanism for text-to-graph conversion. The conversion from text to graph is a research area with technical challenges, where the core … Read more
Training Materials September 2024 Issue This video is a community sharing report from the AI-themed discussion meeting of the Yunhan Community held on August 3, 2024. Speaker: Chen Xiaoyang (Member of the Yunhan Community Committee, General Manager of Fucheng Company) Report Title: “Graph RAG – When Knowledge Graphs Meet Large Models“ AI Video Summary: This … Read more
Source: DeepHub IMBA This article is approximately 2600 words long and is recommended to be read in 8 minutes. For hallucinations in large language models (LLM), knowledge graphs have proven to be superior to vector databases. When using large language models (LLMs), hallucination is a common issue. LLMs generate fluent and coherent text but often … Read more
1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … Read more
New Intelligence Report Source: Google AI Editor: LRS [New Intelligence Guide] Based on pre-training experience, more data leads to better performance! Google recently published a paper at NAACL 2021 that can automatically generate text data from knowledge graphs, so there’s no need to worry about insufficient corpora anymore! Large pre-trained natural language processing (NLP) models, … Read more
Author: Gao Kaiyuan School: Shanghai Jiao Tong University Research Direction: Natural Language Processing Zhihu Column: BERT on the Shoulders of Giants Original Article Link: https://zhuanlan.zhihu.com/p/91052495 Introduction In the previous blog, I discussed some knowledge representation learning models. Today, let’s explore the current most popular BERT model and how it develops with the addition of external … Read more
Author丨Zhou Peng Affiliation丨Tencent Research Direction丨Natural Language Processing, Knowledge Graph Background In the past two years, unsupervised pre-trained language representation models such as Google’s BERT have achieved remarkable results in various NLP tasks. These models are pre-trained on large-scale open-domain corpora to obtain general language representations and then fine-tuned on specific downstream tasks to absorb domain-specific … Read more
Many practitioners have found that although RAG can quickly build a demo in a short time, it faces numerous challenges in actual production environments. This article analyzes the core issue of RAG’s industrial implementation from the perspective of entrepreneurs in the AI large model field—problem grading—and discusses the challenges and solutions of four types of … Read more
Reprinted from WeChat Official Account | Blue’s Little Firefly In this article, I would like to accurately introduce the application areas of Knowledge Graphs (KG) in the RAG pipeline. We will explore the different types of questions that arise in the RAG pipeline and how to address these issues by applying knowledge graphs at various … Read more
On the Chaoxing and Zhihuishu course platforms, there are application sections based on the OBE concept for knowledge graphs. Chaoxing refers to it as the target map, while Zhihuishu calls it OBE teaching management. Both are forms of application that measure, evaluate, and provide feedback on the achievement of course objectives under the guidance of … Read more