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
Source: DeepHub IMBA This article is about 1000 words long and is recommended to read in 5 minutes. When real information exceeds a few words, the chance of exact matching becomes too slim. In RAG systems, actual fact recall evaluation may face the following issues: There has not been much attention paid to automatically verifying … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The Vision of the Community is to promote communication and progress between the academic community, industry, and enthusiasts in machine learning and natural language processing, especially for beginners. … Read more
In the blink of an eye, 2024 is nearing its end. This year, the “Huawei Expert Live Room” has successfully held 7 live broadcasts, sharing Huawei’s experience in industry digital transformation, covering construction, steel, non-ferrous metals, smelting, transportation, oil and gas, and continuously shaping the brand image of “digital transformation partners”. Recently, some friends commented … Read more
Interpretation | Xu Anlong (School of Life Sciences, Beijing University of Chinese Medicine) The key mechanism that enables the human adaptive immune system to recognize diverse antigens and produce specific antibodies is the recombination activating genes RAG1 and RAG2 (collectively known as RAG), which encode the recombinase RAG that catalyzes V(D)J recombination to form diverse … Read more
Detailed steps and code are provided for how to choose the best embedding model and reranker model. When building a Retrieval-Augmented Generation (RAG) pipeline, one of the key components is the retriever. We have various embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers available from CohereAI … Read more
Source: DeepHub IMBA This article is approximately 4600 words long and is recommended to be read in 10 minutes. In this article, we will combine text and knowledge graphs to enhance the performance of our RAG. In our previous articles, we introduced examples of combining knowledge graphs with RAG. In this article, we will combine … Read more
Source: DeepHub IMBA This article is about 3000 words long and is recommended to be read in 5 minutes. This article will explore three effective techniques to enhance document retrieval in applications based on <strong>RAG</strong>. By combining these techniques, it is possible to retrieve documents that closely match user queries, thus generating better answers. The … Read more
Source: ZHUAN ZHI This article is approximately 1000 words long and is recommended for a 5-minute read. In this tutorial, we provide a comprehensive review of the existing research on Retrieval-Augmented Large Language Models (RA-LLMs). As one of the most advanced technologies in the field of artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable … Read more
Source: DeepHub IMBA This article is about 2700 words long, and it is recommended to read it in 5 minutes. This article mainly introduces 3 commonly used methods in Langchain. There are various methods to enhance the capabilities of Retrieval-Augmented Generation (RAG), one of which is called query expansion. Here we mainly introduce 3 commonly … Read more