AllenNLP

Differences and Connections Between RAG and Agentic RAG

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Differences and Connections Between RAG and Agentic RAG

RAG (Retrieval-Augmented Generation) and Agentic RAG primarily differ in their functional scope and execution methods. Here is a detailed comparison: 1. RAG (Retrieval-Augmented Generation): Combines retrieval and generation. The system retrieves relevant information from external knowledge bases and uses generative models (like GPT) to generate answers based on the retrieval results. ① Passivity: Generates answers … Read more

Best Overview of LLM Agents and Agentic RAG

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Best Overview of LLM Agents and Agentic RAG

Paper link: https://arxiv.org/abs/2501.09136 Github repository: https://github.com/asinghcsu/AgenticRAG-Survey Many links below are in this Github repository, and you can access more information by visiting the Github repository. Abstract Agentic Retrieval-Augmented Generation (Agentic RAG) represents a significant leap in the field of artificial intelligence by embedding autonomous agents within the RAG pipeline.This repository supplements the review paper “Agentic … Read more

Transformers as Support Vector Machines: A New Perspective

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Transformers as Support Vector Machines: A New Perspective

Click belowCard, follow the “CVer” public account AI/CV heavy content, delivered first time Click to enter—>【Object Detection and Transformer】 group chat Reprinted from: Machine Heart | Edited by: Egg Sauce, Xiao Zhou SVM is all you need, support vector machines never go out of style. Transformer is a support vector machine (SVM), a new theoretical … Read more

Introducing ∞-former: Infinite Long-Term Memory for Any Length Context

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Introducing ∞-former: Infinite Long-Term Memory for Any Length Context

Reported by Machine Heart Machine Heart Editorial Team Can it hold context of any length? Here is a new model called ∞-former. In the past few years, the Transformer has dominated the entire NLP field and has also crossed into other areas such as computer vision. However, it has its weaknesses, such as not being … Read more

Thoughts on Upgrading Transformer: Simple Considerations on Multimodal Encoding Positions

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Thoughts on Upgrading Transformer: Simple Considerations on Multimodal Encoding Positions

©PaperWeekly Original · Author | Su Jianlin Affiliation | Scientific Space Research Direction | NLP, Neural Networks In the second article of this series, “The Path of Transformer Upgrade: A Rotational Position Encoding that Draws on the Strengths of Many,” the author proposes Rotational Position Encoding (RoPE) — a method to achieve relative position encoding … Read more

Changes in Transformer Architecture Since 2017

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Changes in Transformer Architecture Since 2017

Reading articles about LLMs, you often see phrases like “we use the standard Transformer architecture.” But what does “standard” mean, and has it changed since the original paper? Interestingly, despite the rapid growth in the NLP field over the past five years, the Vanilla Transformer still adheres to the Lindy Effect, which suggests that the … Read more

Fourier Transform Replaces Transformer Self-Attention Layer

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Fourier Transform Replaces Transformer Self-Attention Layer

Machine Heart reports Machine Heart Editorial Team The research team from Google indicates that replacing the transformer self-attention layer with Fourier Transform can achieve 92% accuracy on the GLUE benchmark, with training times 7 times faster on GPU and 2 times faster on TPU. Since its introduction in 2017, the Transformer architecture has dominated the … Read more

The Unsung Heroes Behind Sora? A Detailed Look at the Popular DiT: Embracing Transformer Diffusion Models

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The Unsung Heroes Behind Sora? A Detailed Look at the Popular DiT: Embracing Transformer Diffusion Models

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience covering NLP graduate students, university professors, and industry researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. … Read more

Why Is Your Saved BERT Model So Large?

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Why Is Your Saved BERT Model So Large?

Follow the public account “ML_NLP” Set as “Starred”, heavy content delivered first-hand! Produced by Machine Learning Algorithms and Natural Language Processing Original Column Author on Public Account Liu Cong School | NLP Algorithm Engineer A while ago, a friend asked me this question: the ckpt file size of the bert-base model provided by Google is … Read more

Understanding BERT: Interview Questions and Insights

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Understanding BERT: Interview Questions and Insights

Follow the WeChat public account “ML_NLP“ Set as “starred” for heavy content delivery! Author | Adherer Organizer | NewBeeNLP Interview tips knowledge compilation series, continuously updated Full of valuable content, recommended to collect, or as usual, see you in the background (code: BT) 1. What Is the Basic Principle of BERT? BERT comes from Google’s … Read more