Text Embeddings

No Manual Annotation Needed! LLM-Enhanced Text Embedding Learning: Easily Supporting 100 Languages and Adapting to Hundreds of Thousands of Downstream Tasks

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No Manual Annotation Needed! LLM-Enhanced Text Embedding Learning: Easily Supporting 100 Languages and Adapting to Hundreds of Thousands of Downstream Tasks

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, reaching an audience of NLP graduate students, professors, and researchers in enterprises. The vision of the community is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for beginners. … Read more

Multi-Head RAG: Multi-Head Attention Activation Layer for Document Retrieval

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Multi-Head RAG: Multi-Head Attention Activation Layer for Document Retrieval

Source: DeepHub IMBA This article is about 2500 words long and suggests a reading time of 9 minutes. This paper proposes a new scheme that utilizes the multi-head attention layer of the decoder model instead of the traditional feed-forward layer activation. The existing RAG solutions may suffer because the embeddings of the most relevant documents … Read more

Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

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Integrating Text and Knowledge Graph Embeddings to Enhance RAG Performance

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

Illustrated Word2Vec: A Comprehensive Guide

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Illustrated Word2Vec: A Comprehensive Guide

Natural Language Processing Author: Machine Learning Beginner Original Author: Jalammar, Translated by Huang Haiguang Since 2013, word2vec has been an effective method for word embedding. This article presents word2vec in an illustrated manner, with no mathematical formulas, making it very easy to understand, and is recommended for beginners to read. (Original Author: jalammar, Translation: Huang … Read more

RAG: From Theory to LlamaIndex Practice (Detailed Version)

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RAG: From Theory to LlamaIndex Practice (Detailed Version)

Abstract Large language models (LLMs) have demonstrated impressive capabilities. However, this does not mean they are error-free; anyone who has experienced ChatGPT’s “hallucinations” can attest to that. Retrieval Augmented Generation (RAG) is a framework designed to make LLMs more reliable by extracting relevant, up-to-date data directly related to user queries. In this article, I analyze … Read more