TensorWave的动态

pub.towardsai.net: The content introduces the concept of Large Language Models (LLMs) and their applications in Retrieval-Augmented Generation (RAG) using open-source LLMs like Mistral and Llama. It provides a detailed guide on implementing RAG using Llama-3 and Langchain, including setting up the model, dependencies, and a text generation pipeline. The content also discusses the complexities of the prompt format and demonstrates how to build a simple RAG system using Llama-3 and Langchain. The post concludes with an example of using the developed RAG pipeline to interact with the Llama-3 model.

How I Enhanced Large Language Models With Simple RAG... RAG can provide updated and domain-specific knowledge, returning more targeted results that reduce the need for fine-tuning and limiting hallucinations.

a reasoning large language model https://lnkd.in/gpRrXzgv

Retrieval Augmented Generation (RAG) techniques with large language models (LLMs) to alleviate the limitations of LLMs, such as hallucination and out-of-date internal knowledge. A very good paper .... https://lnkd.in/eiZCTB2c

Large Language Models (LLMs) face issues like hallucination and outdated knowledge, but Retrieval-Augmented Generation (RAG) enhances their accuracy and reliability by integrating external databases. This review explores RAG's evolution, key techniques, and future research directions. I’d compare Retrieval-Augmented Generation (RAG) to Iron Man. Just like Iron Man combines his genius and technology to tackle challenges with precision, intelligence and innovation, RAG enhances LLMs with external databases to improve accuracy and continuously update knowledge, making them more powerful and reliable. #learning #genAI #rag https://lnkd.in/dD-4VxEP

I've been delving into Large Language Models (LLMs), searching for the most effective model to fine-tune. During my research, I came across an incredibly insightful article: "The Practical Guide to LLMs: Falcon." ?? If you're on a similar journey or just curious about LLMs, I highly recommend checking out this article: #LLM

My paper on probabilistic errors in large language models has come out in Synthese. https://lnkd.in/dEVmQ6Gy There is also a read-only free version of the paper accessible here: https://rdcu.be/dG8Zc

Retrieval-Augmented Generation (RAG) approach, where a retriever suggests relevant steps and table names based on the user's natural language input. These suggestions are then incorporated into the LLM prompt to guide the generation of the structured JSON output representing the workflow. https://lnkd.in/eUexurQC #rag #LLMs

https://lnkd.in/g2-_QZ4E "novel and versatile thought-augmented reasoning approach for enhancing accuracy, efficiency and robustness of large language models (LLMs)"

less words and more actions... "Unlike their predecessors, Large Language Models (LLMs), which mainly focus on understanding and generating text, LAMs are designed to understand and replicate complex user tasks, ranging from web navigation to application-specific operations." https://lnkd.in/gSyUW3v3