AWS Machine Learning Blog

JANUARY 19, 2024

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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Marktechpost

JULY 4, 2023

Computer programs called large language models provide software with novel options for analyzing and creating text. It is not uncommon for large language models to be trained using petabytes or more of text data, making them tens of terabytes in size.

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Unite.AI

MAY 29, 2024

This advancement has spurred the commercial use of generative AI in natural language processing (NLP) and computer vision, enabling automated and intelligent data extraction. Additionally, it poses a security risk when handling sensitive data, making it a less desirable option in the age of automation and digital security.

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AssemblyAI

MAY 23, 2023

ChatGPT is part of a group of AI systems called Large Language Models (LLMs) , which excel in various cognitive tasks involving natural language. Industry leaders like Microsoft and Google recognize the importance of LLMs in driving innovation, automation, and enhancing user experiences.

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AWS Machine Learning Blog

FEBRUARY 6, 2024

To support overarching pharmacovigilance activities, our pharmaceutical customers want to use the power of machine learning (ML) to automate the adverse event detection from various data sources, such as social media feeds, phone calls, emails, and handwritten notes, and trigger appropriate actions.

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Unite.AI

JANUARY 23, 2024

Large language models (LLMs) have exploded in popularity over the last few years, revolutionizing natural language processing and AI. What are Large Language Models and Why are They Important? Techniques like Word2Vec and BERT create embedding models which can be reused.

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ODSC - Open Data Science

SEPTEMBER 6, 2023

Most people who have experience working with large language models such as Google’s Bard or OpenAI’s ChatGPT have worked with an LLM that is general, and not industry-specific. But as time has gone on, many industries have realized the power of these models. This is where BioBERT comes in.

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Marktechpost

JULY 1, 2023

Large Language Models (LLMs) have proven to be really effective in the fields of Natural Language Processing (NLP) and Natural Language Understanding (NLU). Famous LLMs like GPT, BERT, PaLM, etc., Being trained on massive amounts of datasets, these LLMs capture a vast amount of knowledge.

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Marktechpost

FEBRUARY 12, 2024

LLMWare is setting out to uniquely address all three of these challenges with the launch of its 1B parameter small language models called SLIMs ( S tructured L anguage I nstruction M odels) and a new set of capabilities in the LLMWare library to execute multi-model, multi-step agent workflows in private cloud.

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Marktechpost

MAY 13, 2024

Tokenization is essential in computational linguistics, particularly in the training and functionality of large language models (LLMs). This process involves dissecting text into manageable pieces or tokens, which is foundational for model training and operations. If you like our work, you will love our newsletter.

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Marktechpost

DECEMBER 5, 2023

Large Language Models (LLMs), like GPT, PaLM, LLaMA, etc., Their ability to utilize the strength of Natural Language Processing, Generation, and Understanding by generating content, answering questions, summarizing text, and so on have made LLMs the talk of the town in the last few months.

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John Snow Labs

JUNE 27, 2023

Prepare to be amazed as we delve into the world of Large Language Models (LLMs) – the driving force behind NLP’s remarkable progress. In this comprehensive overview, we will explore the definition, significance, and real-world applications of these game-changing models. What are Large Language Models (LLMs)?

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Unite.AI

FEBRUARY 23, 2024

Prompt engineering is the art and science of crafting inputs (or “prompts”) to effectively guide and interact with generative AI models, particularly large language models (LLMs) like ChatGPT. teaches students to automate document handling and data extraction, among other skills.

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IBM Journey to AI blog

FEBRUARY 13, 2024

Automate tedious, repetitive tasks. This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Best practices are evolving rapidly.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

Reports holistically summarize each evaluation in a human-readable way, through natural-language explanations, visualizations, and examples, focusing annotators and data scientists on where to optimize their LLMs and help make informed decisions. What is FMEval?

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Heartbeat

MAY 29, 2023

LLMs are one of the most exciting advancements in natural language processing (NLP). This technique is commonly used in neural network-based models such as BERT, where it helps to handle out-of-vocabulary words. There are several pre-trained LLMs available that can be used for transfer learning, such as GPT-2, BERT, and RoBERTa.

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Viso.ai

JULY 18, 2024

What are Large Language Models (LLMs)? In generative AI, human language is perceived as a difficult data type. If a computer program is trained on enough data such that it can analyze, understand, and generate responses in natural language and other forms of content, it is called a Large Language Model (LLM).

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AWS Machine Learning Blog

MARCH 6, 2023

Language models are statistical methods predicting the succession of tokens in sequences, using natural text. Large language models (LLMs) are neural network-based language models with hundreds of millions ( BERT ) to over a trillion parameters ( MiCS ), and whose size makes single-GPU training impractical.

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IBM Journey to AI blog

NOVEMBER 24, 2023

Many enterprises are realizing that moving to cloud is not giving them the desired value nor agility/speed beyond basic platform-level automation. Generative AI-based Solution Approach : The Mule API to Java Spring boot modernization was significantly automated via a Generative AI-based accelerator we built.

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ODSC - Open Data Science

MARCH 10, 2025

MLOps, Ethical AI, and the Rise of Large Language Models (20202022) The global shift to remote work during the pandemic accelerated interest in MLOps a set of practices for deploying, monitoring, and scaling machine learning models. The real game-changer, however, was the rise of Large Language Models (LLMs).

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Unite.AI

DECEMBER 27, 2023

OpenAI's GPT-4 stands as a state-of-the-art generative language model, boasting an impressive over 1.7 trillion parameters, making it one of the largest language models ever created. Its applications range from chatbots to content creation and language translation.

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Snorkel AI

MAY 25, 2023

Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment. But what are large language models? Table of contents What are large language models (LLMs)? Their new model combined several ideas into something surprisingly simple and powerful.

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Snorkel AI

MAY 25, 2023

Since the public unveiling of ChatGPT, large language models (or LLMs) have had a cultural moment. But what are large language models? Table of contents What are large language models (LLMs)? Their new model combined several ideas into something surprisingly simple and powerful.

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Unite.AI

JULY 9, 2024

LLM watermarking, which integrates imperceptible yet detectable signals within model outputs to identify text generated by LLMs, is vital for preventing the misuse of large language models. These watermarking techniques are mainly divided into two categories: the KGW Family and the Christ Family. So let’s get started.

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AWS Machine Learning Blog

JUNE 25, 2024

Large language models (LLMs) have transformed the way we engage with and process natural language. These powerful models can understand, generate, and analyze text, unlocking a wide range of possibilities across various domains and industries. This provides an automated deployment experience on your AWS account.

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ODSC - Open Data Science

MARCH 12, 2025

MLOps emerged as a necessary discipline to address the challenges of deploying and maintaining machine learning models in production environments. Initially, organizations struggled with versioning, monitoring, and automating model updates.

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Marktechpost

JULY 22, 2024

With deep learning models like BERT and RoBERTa, the field has seen a paradigm shift. Therefore, AV models must be accurate and interpretable, providing detailed insights into their decision-making processes. Existing methods for AV have advanced significantly with the use of deep learning models. Check out the Paper.

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Marktechpost

SEPTEMBER 28, 2024

In the age of data-driven artificial intelligence, LLMs like GPT-3 and BERT require vast amounts of well-structured data from diverse sources to improve performance across various applications. While these tools are capable of collecting web data, they often do not format the output in a way that LLMs can easily process.

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IBM Journey to AI blog

JULY 19, 2023

The industry is under tremendous pressure to accelerate drug development at an optimal cost, automate time- and labor-intensive tasks like document or report creation to preserve employee morale, and accelerate delivery. Yet, it is burdened by long R&D cycles and labor-intensive clinical, manufacturing and compliancy regimens.

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Heartbeat

JANUARY 9, 2024

Large language models have emerged as ground-breaking technologies with revolutionary potential in the fast-developing fields of artificial intelligence (AI) and natural language processing (NLP). These LLMs are artificial intelligence (AI) systems trained using large data sets, including text and code.

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Marktechpost

APRIL 1, 2024

Although large language models (LLMs) had been developed prior to the launch of ChatGPT, the latter’s ease of accessibility and user-friendly interface took the adoption of LLM to a new level. It provides codes for working with various models, such as GPT-4, BERT, T5, etc., and explains how they work.

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Heartbeat

AUGUST 2, 2023

Introduction to LLMs LLM in the sphere of AI Large language models (often abbreviated as LLMs) refer to a type of artificial intelligence (AI) model typically based on deep learning architectures known as transformers. The end goal of such a model is to understand and be able to generate human-like text.

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Topbots

APRIL 11, 2023

In this article, we will delve into the latest advancements in the world of large-scale language models, exploring enhancements introduced by each model, their capabilities, and potential applications. The Most Important Large Language Models (LLMs) in 2023 1. billion word corpus).

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Bugra Akyildiz

DECEMBER 28, 2024

Articles Speculative decoding is a technique that can significantly improve the inference for large language models (LLMs) by reducing latency and improving efficiency without compromising output quality. DRAGON can be used as a drop-in replacement for BERT.

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IBM Journey to AI blog

AUGUST 1, 2023

For example, a foundation model might be used as the basis for a generative AI model that is then fine-tuned with additional manufacturing datasets to assist in the discovery of safer and faster ways to manufacturer a type of product. An open-source model, Google created BERT in 2018.

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AWS Machine Learning Blog

AUGUST 2, 2024

Foundational large language models (LLMs) alone are not suitable to model such data because the underlying data distributions and relationships don’t correspond to what LLMs learn from their pre-training data corpuses. GraphStorm provides different ways to fine-tune the BERT models, depending on the task types.

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Marek Rei

NOVEMBER 4, 2024

It is probably good to also to mention that I wrote all of these summaries myself and they are not generated by any language models. They focus on coherence, as opposed to correctness, and develop an automated LLM-based score (BooookScore) for assessing summaries. Are Emergent Abilities of Large Language Models a Mirage?

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