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Supercharging Graph Neural Networks with Large Language Models: The Ultimate Guide

Unite.AI

In parallel, Large Language Models (LLMs) like GPT-4, and LLaMA have taken the world by storm with their incredible natural language understanding and generation capabilities. In this article, we will delve into the latest research at the intersection of graph machine learning and large language models.

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Will Large Language Models End Programming?

Unite.AI

In areas like image generation diffusion model like Runway ML , DALL-E 3 , shows massive improvements. The post Will Large Language Models End Programming? The rapid advancements in AI, are not limitd to text/code generation. Just see the below tweet by Runway showcasing their latest feature.

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Bridging Large Language Models and Business: LLMops

Unite.AI

LLMOps versus MLOps Machine learning operations (MLOps) has been well-trodden, offering a structured pathway to transition machine learning (ML) models from development to production. The cost of inference further underscores the importance of model compression and distillation techniques to curb computational expenses.

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The Future of Serverless Inference for Large Language Models

Unite.AI

Recent advances in large language models (LLMs) like GPT-4, PaLM have led to transformative capabilities in natural language tasks. Prominent implementations include Amazon SageMaker, Microsoft Azure ML, and open-source options like KServe.

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BiomedRAG: Elevating Biomedical Data Analysis with Retrieval-Augmented Generation in Large Language Models

Marktechpost

The emergence of large language models (LLMs) has profoundly influenced the field of biomedicine, providing critical support for synthesizing vast data. These models are instrumental in distilling complex information into understandable and actionable insights. on the GIT and ChemProt datasets, respectively.

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The Vulnerabilities and Security Threats Facing Large Language Models

Unite.AI

Large language models (LLMs) like GPT-4, DALL-E have captivated the public imagination and demonstrated immense potential across a variety of applications. However, these promising models also pose novel vulnerabilities that must be addressed.

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Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review

Marktechpost

Graph Machine Learning (Graph ML), especially Graph Neural Networks (GNNs), has emerged to effectively model such data, utilizing deep learning’s message-passing mechanism to capture high-order relationships. Alongside topological structure, nodes often possess textual features providing context.