AI knowledge Page 8

This eBook, "How to Build a Career in Artificial Intelligence", written by Andrew Ng, founder of DeepLearning.AI, is a comprehensive guide on how to build and develop a career in AI by learning to program, working on projects, finding jobs, and other steps. Introduction This is a career development...

ToolGen is a framework for integrating tool knowledge directly into large-scale language models (LLMs), enabling seamless tool invocation and language generation by representing each tool as a unique token. It was developed by Renxi Wang et al. to improve the performance of tool retrieval and task completion. Tool markup ...

  Despite the continuous release of ever larger and smarter models, state-of-the-art generative Large Language Models (LLMs) still suffer from a major problem: they perform poorly when dealing with tasks that require specialized knowledge. This lack of expertise can lead to problems such as the phenomenon of hallucination, whereby model generation...

Introduction As the field of Artificial Intelligence (AI) continues to grow, cue engineering has become a promising career. Today, many people are striving to acquire the skills to interact effectively with Large Language Models (LLMs). Do you share the same desire? Are thinking about where to start and how to proceed? We offer...

RAG stands for Retrieval Augmented Generation. Let's break down this terminology to get a clearer picture of what RAG is: R -> Retrieval A -> Augmentation G -> Generation Basically, the Large Language Model (LLM) we are using now is not real...

Introduction This study introduces Agora, a metacommunication protocol designed for Large Language Model (LLM) networks.Agora aims to solve the trilemma between efficiency, scalability, and flexibility when communicating between agents by using a mixture of natural language and structured protocols to achieve efficient, scalable, and flexible...

Showcasing how top companies, governments, researchers and startups are utilizing Google's AI solutions to enhance their work. This post was originally published on the Transform with Google Cloud blog. First published on April 12, 2024; latest use case update on September 24, 2024 . Since ...

The OpenAI o1 family of models is a new class of large language models trained through reinforcement learning and designed to perform complex reasoning. o1 models think before they answer and are capable of generating long internal chains of thought before responding to the user. o1 models excel at scientific reasoning and perform well on competitive programming problems (Cod...

An Alternative to Transformer in Language Modeling The Transformer architecture is a key component of the success of large language models (LLMs). Almost all large language models in use today employ this architecture, from open source models such as Mistral to closed source models such as ChatGPT. To further improve ...

ChatGPT memory function has been open for a long time, many friends do not understand its characteristics. Memory is not just an on/off switch, it can't be simply understood that if you turn it on, it remembers the history messages, and if you turn it off, it doesn't remember the history messages. Principle of Memory Function In GPT4o system command, ChatGPT preset prompt word command is what...

Original article: https://arxiv.org/abs/2409.09030 Abstract In recent years, Large Language Models (LLMs) have enjoyed remarkable success and have been widely used in a variety of downstream tasks, especially in tasks in the software engineering (SE) domain. We have found that many studies that combine LLMs with SE...

As we all know, when we need to let a large language model execute a task, we need to input a Prompt to guide its execution, which is described using natural language. For simple tasks, natural language can describe them clearly, such as: "Please translate the following into Simplified Chinese:", "Please generate the...

Good morning, everyone! I'm Louis-Francois, co-founder and CTO of Towards AI, and today we're going to dive into what may be the best Retrieval-Augmented Generation (RAG) stack available today -- thanks to the fascinating research of Wang et al. (2024). This research provides a treasure trove for building optimal RAG systems...