LLMs

How Large Models Understand Video? A Review of MM-LLMs in Long Video Comprehension

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How Large Models Understand Video? A Review of MM-LLMs in Long Video Comprehension

MLNLP community is a well-known machine learning and natural language processing community in China and abroad, 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 and industrial fields of natural language processing and machine learning, especially for beginners. Reprinted … Read more

Performance Improvement with Pseudo-Graph Indexing for RAG

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Performance Improvement with Pseudo-Graph Indexing for RAG

This article is approximately 5500 words long and is recommended for an 11-minute read. This paper proposes a pseudo-graph structure by relaxing the pattern constraints on data and relationships in traditional KGs. Paper Title: Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning Author Affiliation: Renmin University of China (RUC), Shanghai … Read more

RAG Meets LLMs: Advancing Retrieval-Augmented Large Language Models

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RAG Meets LLMs: Advancing Retrieval-Augmented Large Language Models

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

Recent Advances in RAG for Large Models

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Recent Advances in RAG for Large Models

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 professors, and researchers from enterprises. The vision of the community is to promote communication and progress between the academic and industrial worlds of natural language processing and machine learning, especially for the … Read more

Li Feifei Team Refutes AGI: Do LLMs Have Perception?

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Li Feifei Team Refutes AGI: Do LLMs Have Perception?

Author:Los Since the release of ChatGPT in November 2022, LLMs have been the most controversial “hot topic” in the field of artificial intelligence. While often referred to as the era of large models, it should more accurately be called the “era of large language models,” or the LLMs era. This year, the tech world has … Read more

Complete Guide to Agents: The Revolution of LLMs and Intelligent Applications

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Complete Guide to Agents: The Revolution of LLMs and Intelligent Applications

1. Complete Guide to Agents: The Revolution of LLMs and Intelligent Applications The next evolution of AI-driven software is not chatbots, but applications that utilize LLMs to perform real work. This eBook from the AI Infrastructure Alliance comprehensively covers various aspects of this field, including Prompt Engineering, LLM logic and reasoning, major frameworks such as … Read more

Impact of Irrelevant Inputs on LLMs in RAG Systems

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Impact of Irrelevant Inputs on LLMs in RAG Systems

Introduction Hello everyone, I am Liu Cong from NLP. RAG (Retrieval-Augmented Generation) finds information fragments relevant to user questions through a retrieval system, utilizing large models to synthesize an answer. This greatly addresses issues such as hallucination and outdated information in large models, and has become an important means for the practical application of large … Read more

Combining RAG and LLMs: A Review of Retrieval-Augmented Large Language Models

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Combining RAG and LLMs: A Review of Retrieval-Augmented Large Language Models

As one of the most advanced technologies in artificial intelligence, Retrieval-Augmented Generation (RAG) technology can provide reliable and up-to-date external knowledge, offering great convenience for numerous tasks. Especially in the era of AI-Generated Content (AIGC), RAG’s powerful retrieval capabilities in providing additional knowledge enable it to assist existing generative AI in producing high-quality outputs. Recently, … Read more

Enhancement Techniques for Large Model Retrieval (RAG)

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Enhancement Techniques for Large Model Retrieval (RAG)

Click the bottom “Read Original” to browse the detailed content of “CCF Digital Focus” Issue 48 Editor’s Note Large language models (LLMs) still face many challenges when dealing with domain-specific or knowledge-intensive tasks, such as generating hallucinations, outdated knowledge, and opaque, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) technology has emerged to address these issues. RAG … Read more

Overview of Agentic Retrieval-Augmented Generation

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Overview of Agentic Retrieval-Augmented Generation

Large language models (LLMs) have revolutionized the field of artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their responsiveness to dynamic real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) serves as a solution by integrating real-time data retrieval to enhance … Read more