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Evaluate large language models for your machine translation tasks on AWS

AWS Machine Learning Blog

Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. One of LLMs most fascinating strengths is their inherent ability to understand context.

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Enhancing Customer Support Efficiency Through Automated Ticket Triage 

Analytics Vidhya

Leveraging Large Language Models (LLMs) such as OpenAI’s GPT-3.5 This article explores the application of LLMs in automating ticket triage, providing a seamless and efficient solution for customer support teams. Introduction In the fast-paced world of customer support efficiency and responsiveness are paramount.

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Best Large Language Models & Frameworks of 2023

AssemblyAI

However, among all the modern-day AI innovations, one breakthrough has the potential to make the most impact: large language models (LLMs). Large language models can be an intimidating topic to explore, especially if you don't have the right foundational understanding. What Is a Large Language Model?

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Researchers from Fudan University and Shanghai AI Lab Introduces DOLPHIN: A Closed-Loop Framework for Automating Scientific Research with Iterative Feedback

Marktechpost

Several research environments have been developed to automate the research process partially. improvement over baseline models. to close the gap between BERT-base and BERT-large performance. In sentiment classification, DOLPHIN improved accuracy by 1.5% The success rate of debugging went from 33.3%

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Deploying Large Language Models on Kubernetes: A Comprehensive Guide

Unite.AI

Large Language Models (LLMs) are capable of understanding and generating human-like text, making them invaluable for a wide range of applications, such as chatbots, content generation, and language translation. Large Language Models (LLMs) are a type of neural network model trained on vast amounts of text data.

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LLMOps: The Next Frontier for Machine Learning Operations

Unite.AI

MLOps are practices that automate and simplify ML workflows and deployments. MLOps make ML models faster, safer, and more reliable in production. But more than MLOps is needed for a new type of ML model called Large Language Models (LLMs). However, LLMs are also very different from other models.

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Reduce inference time for BERT models using neural architecture search and SageMaker Automated Model Tuning

AWS Machine Learning Blog

In this post, we demonstrate how to use neural architecture search (NAS) based structural pruning to compress a fine-tuned BERT model to improve model performance and reduce inference times. First, we use an Amazon SageMaker Studio notebook to fine-tune a pre-trained BERT model on a target task using a domain-specific dataset.

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